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Senior Data Scientist, Full Stack

TRM Labs

Location: Canada, USA Advertised pay: USD 180,000–210,000 / yearly Work arrangement: Region-restricted remote Employment: Full Time Posted: October 29, 2026 Freshness: Very fresh Source check: Observed in the past 3 days
Disclosure4 of 5 core facts statedReview uncertainties
Last source observationObserved in the past 3 daysObservation is not employer confirmation.
Application destinationjobicy.comReview the application route and source evidence in the full report below.

Before relying on the listing: Confirm Application closing time with the employer or source. See verification questions

Executive opportunity brief

The decision in one screen

A compact evidence-first read of compensation, flexibility, mobility and what still needs verification. No universal quality score is used.

Compensation positionAbove the current median52 current same-currency observations
Work flexibilityRegion-restricted remoteBased only on stated location/work-arrangement evidence.
Decision evidence4 of 5 core facts knownThis listing states more core decision facts than the median role in the current sample.
Mobility evidenceSponsorship not establishedUnknown evidence is not treated as unavailable.
Current alternatives1196 current same-family rolesCurrent HPJ inventory, not the whole labor market.
Negotiation readinessNegotiation evidence available: advertised pay range, current same-currency market context, strong decision evidence.Use the private Offer & Negotiation Lab below if an offer arrives.

Fast read

Opportunity DNA

The available details do not point to a single standout advantage yet. The clearest issue to weigh is location rules could change the fit.

PayRange available

Current comparable position is around the 56th percentile.

FlexibilityLocation limits visible

Geographic eligibility is restricted to a country or region.

Entry pathRequirements unclear

The listing does not make the minimum education or experience threshold clear enough yet.

MobilityNot established

Sponsorship and relocation support are not clear from the current listing.

ApplicationMore friction visible

3 explicit steps are visible before or during application.

TraceabilityCurrent source traceable

At least one recent enabled source can still renew this vacancy.

This fingerprint summarizes what is clear, constrained or still unanswered in the current listing. It is a reading aid, not an overall job score.

Decision evidence check

4 of 5 core facts stated

This checks whether the listing states the facts that commonly change an employment decision. It measures disclosure completeness, not whether the opportunity is good or bad.

Facts already stated

  • Compensation
  • Work arrangement
  • Location eligibility
  • Employment type

What to verify next

  • Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?

Unknown information is never converted into a negative score. The questions above exist to prevent missing evidence from being mistaken for a favorable term.

Decision levers

What could make or break the fit?

Reasons to look closer

Nothing stands out strongly enough yet to call a clear advantage. The role may still be attractive for reasons the listing does not spell out.

Trade-offs to weigh

  • Location rules could change the fit: Geographic eligibility is restricted to a country or region.
  • The application route has several visible steps: 3 application steps are stated in the listing.

Questions worth asking before you invest more time

  • Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
  • Confirm the actual minimum experience requirement rather than assuming preferred background is mandatory.
  • Ask whether visa sponsorship is available before investing significant time in the application.

These questions come from details the listing leaves open. An unanswered point is not treated as a disadvantage.

Opportunity neighborhood

Keep your options open

These live roles sit close enough to this opportunity to make useful comparison points. Look across several employers before deciding what pay, flexibility or access trade-offs you are willing to accept.

FDE Data Engineer- Space (FedD141/FedD147)

Defense Unicorns · USA

USD 123,250–166,750 / year Region-restricted remote 4/5 core facts

Why it is nearby: Same seniority band · Same country · Similar work arrangement

About 26% lower advertised midpoint

Sr. Data Scientist

hims & hers · USA

USD 150,000–180,000 / year Region-restricted remote 4/5 core facts

Why it is nearby: Same seniority band · Same country · Similar work arrangement

About 15% lower advertised midpoint

Research Engineer, Machine Learning Systems

Deepgram · USA

USD 150,000–250,000 / year Region-restricted remote 4/5 core facts

Why it is nearby: Same seniority band · Same country · Similar work arrangement

About 3% higher advertised midpoint

Data Engineer (L5) - Ads

Netflix · Remote — Worldwide

USD 380,000–610,000 / year Worldwide remote 5/5 core facts

Why it is nearby: Same seniority band · Similar work arrangement · Same employment type

About 154% higher advertised midpoint

This neighborhood is drawn from current inventory in the same broad occupation. Similarity is based on explicit listing facts; missing details do not count against a job.

Role market context

How much current choice is visible around this kind of work?

This compares the vacancy with current High Paid Job inventory in the broad Data & AI roles family. It describes visible choice and disclosure, not future demand or career quality.

Current visible roles1,196

200 most recent current roles sampled for the disclosure measures below.

Employers represented141

Distinct employers in the bounded current sample, not an estimate of all employers hiring in the market.

Decision-ready disclosure29%

Current sampled roles stating at least four of the five Core Decision Facts.

Compensation stated34%

Requires a numeric amount, explicit ISO currency and supported pay period.

Work arrangement stated93%

Listings with explicit enough remote, hybrid or on-site evidence to classify.

Stated closing time20%

Missing deadline evidence remains unknown and is not interpreted as an open-ended application window.

Disclosure comparison: This listing states more core decision facts than the median role in the current sample. This listing: 4/5 · sample median: 3.0/5.

Currencies with explicit pay evidence in the sample: USD (43) · EUR (17) · GBP (5) · CZK (1). Raw amounts across different currencies are not compared.

This describes current visible High Paid Job inventory in the same broad occupation family. Percentages use a bounded recent sample and are not a forecast, a complete labor-market census or an employer-quality measure.

Constraint opportunity lab

What changes the size of the current opportunity pool?

Each line applies one stricter evidence-based constraint to the same bounded current role-family sample. It helps you see the trade-off in visible choice without deciding which trade-off is right for you.

Require explicit sponsorship or relocation support 2 / 200 1%

Counts only affirmative or possible sponsorship evidence, or relocation explicitly offered. Unknown is not treated as no.

Require worldwide remote evidence 22 / 200 11%

A much stricter subset: only listings with explicit worldwide-remote evidence remain.

Keep this advertised salary floor in the same currency 10 / 43 23%

Only same-currency opportunities with usable annualized pay enter this comparison. Other currencies are not converted or treated as lower.

Require at least four Core Decision Facts 58 / 200 29%

Shows the subset with enough stated evidence for a more informed first comparison.

Require a remote-listed arrangement 65 / 200 33%

Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.

Require usable compensation disclosure 67 / 200 34%

Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.

Stay in the same stated country 36 / 73 49%

Uses only roles where a country is known. Roles without country evidence are excluded from this denominator.

Stay around the same seniority band 176 / 200 88%

Includes roles whose seniority is genuinely unspecified rather than assuming they belong to another level.

These are counterfactual filters over a bounded sample of current HPJ inventory in the same broad occupation family. They show how visible choice changes when one evidence-based constraint is applied; they do not tell you which constraint you should choose or predict hiring outcomes. The calculation uses the 200 most recent current roles from 1196 visible roles in this broad family.

Salary transparency context

Is pay disclosure typical for this kind of role?

This listing states usable structured compensation. About 34% of the bounded current role sample does the same.

This listingUsable pay stated

Compensation counts only when amount, ISO currency and supported pay period are all known.

Current same-role sample34%

Pay disclosure is limited in the current role sample.

Current employer pattern35%

This employer’s current pay-disclosure rate is broadly similar to the current same-role sample.

This compares salary-disclosure behavior in current visible listings. It does not compare raw salary amounts across currencies, infer undisclosed pay as zero, or rate employer quality.

Private offer & negotiation lab

Turn an offer into evidence, not guesswork

Enter an annual base offer and, if you want, your own target. HPJ compares only within one explicit currency and never chooses a target for you.

Advertised annualized payUSD 180,000–210,000 / yearListing evidence only.
Current comparable medianUSD 155,42642 current sampled roles
Current upper quartileUSD 193,750Current HPJ inventory context, not a salary recommendation.

Negotiation evidence available: advertised pay range, current same-currency market context, strong decision evidence.

Enter an annual base offer to compare it with explicit same-currency evidence. HPJ will not suggest a target for you.

Review the full package · 10 terms

Mark what is confirmed, what needs clarification and what you want to prioritize. These choices and notes stay in this browser.

Base payKnown

Usable advertised compensation is available.

Bonus / commissionMentioned

Variable compensation is mentioned; verify amount, formula, target and eligibility.

Equity / long-term incentivesMentioned

Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.

Retirement / pensionVerify

Retirement or pension benefits are mentioned; verify employer contribution and vesting.

Health benefitsMentioned

Health-benefit language is mentioned; verify coverage, dependants and employee cost.

Paid leaveVerify

Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.

Work arrangementKnown

A work-arrangement signal is available; verify schedule details before relying on it.

RelocationVerify

Relocation support not established

Visa / work authorizationVerify

Sponsorship not established

Review timingVerify

A review cycle is mentioned; verify timing and whether compensation can change at that review.

Questions worth taking into the offer conversation

  • What retirement or pension contribution does the employer provide?
  • How much paid leave is provided and what are the carry-over or blackout rules?
  • If relocation is relevant, what expenses, timing and repayment conditions are covered?
  • What exact sponsorship or work-authorization conditions apply to this role?
  • When is the first performance and compensation review after joining?
  • Is there a firm application deadline, a rolling review window or no stated closing date?

Occupation intelligence

What broader career references connect to this role?

HPJ uses broad comparison families for current-market analysis. External taxonomy results are reference candidates for that broad family, not a declaration that this vacancy has been authoritatively classified into a specific occupation.

HPJ comparison familyData & AI roles
Adjacent families worth exploringSoftware engineering roles · Product roles

European occupation references

This service uses the ESCO classification of the European Commission.

U.S. occupation references

This site incorporates information from O*NET Web Services by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). O*NET® is a trademark of USDOL/ETA.

Career reference research

Skills worth investigating

Official occupational context, paired with exact phrases visible in this vacancy. It is not a skills test or an applicant-fit score.

ESCO · European occupation

data scientist

Usually essential in this ESCO occupation
  • statistical modeling techniques
  • perform project management
  • query languages
  • online analytical processing
  • develop data processing applications
  • data engineering
  • manage data collection systems
Context-dependent ESCO skills
  • manage ICT data architecture
  • SPARQL
  • use spreadsheets software
  • computational biology
  • state estimation

O*NET · U.S. occupation

Data Scientists

O*NET occupation skills
  • Mathematics
  • Critical Thinking
  • Reading Comprehension
  • Complex Problem Solving
  • Programming
  • Active Learning
  • Speaking
  • Active Listening
Build my private skill preparation brief Your evidence · Practice · Interview questions

Plan how you might demonstrate or investigate occupational skills. These are broad-family reference examples, not confirmed vacancy requirements. Nothing you enter is sent to High Paid Job.

statistical modeling techniquesESCO · Essential · Listing mention unknown
perform project managementESCO · Essential · Listing mention unknown
query languagesESCO · Essential · Listing mention unknown
online analytical processingESCO · Essential · Listing mention unknown
develop data processing applicationsESCO · Essential · Listing mention unknown
data engineeringESCO · Essential · Listing mention unknown
manage data collection systemsESCO · Essential · Listing mention unknown
manage ICT data architectureESCO · Optional · Listing mention unknown
SPARQLESCO · Optional · Listing mention unknown
use spreadsheets softwareESCO · Optional · Listing mention unknown
computational biologyESCO · Optional · Listing mention unknown
state estimationESCO · Optional · Listing mention unknown
MathematicsO*NET · Skills · Listing mention unknown
Critical ThinkingO*NET · Skills · Listing mention unknown
Reading ComprehensionO*NET · Skills · Listing mention unknown
Complex Problem SolvingO*NET · Skills · Listing mention unknown
ProgrammingO*NET · Skills · Listing mention unknown
Active LearningO*NET · Skills · Listing mention unknown
SpeakingO*NET · Skills · Listing mention unknown
Active ListeningO*NET · Skills · Listing mention unknown

Private to this browser. Up to 20 reference skills; not a qualification assessment or hiring prediction.

Useful next question: Which of these occupational reference skills does the hiring manager actually expect for this specific role, and what evidence would demonstrate them?

These skills belong to one reference occupation selected for HPJ’s broad career family, not a verified classification of this vacancy. Text overlap only means a phrase appears in the available listing text; it does not establish an employer requirement, your proficiency, or a qualification gap. A missing mention is unknown, not a deficit.

Adjacent current roles to investigate

Product roles

Founding Product Manager

infisical · USA

same stated country · same employment type · similar work-arrangement evidence
Software engineering roles

Staff Software Engineer

Bloomreach · Europe

same seniority band · same employment type · similar work-arrangement evidence

Adjacent-family roles are exploration prompts from current HPJ inventory. They are not inferred qualification matches and they do not imply a promotion path.

Career move map

What sits one step away?

This listing is currently read as Senior / lead. Nearby roles one visible seniority step away can help you judge whether to widen the search, stretch upward or compare the responsibilities more carefully.

Broader-entry direction

Mid-career

Data Scientist: Product

Thatch · Remote — Worldwide

USD 160,000–230,000 / year Similar work arrangement · Same employment type · Recently posted Advertised midpoint is roughly level with this role
Mid-career

Data Analyst

Sentra · Remote — Worldwide

Similar work arrangement · Same employment type · Recently posted

Stretch direction

Leadership

Sr. Director of Machine Learning

hims & hers · USA

USD 330,000–360,000 / year Same country · Similar work arrangement · Same employment type Advertised midpoint is about 77% higher
Leadership

GTM AI Engineer

Customer.io · LATAM, Canada, USA

USD 140,000–170,000 / year Same country · Similar work arrangement · Same employment type Advertised midpoint is about 21% lower

These are live roles one visible seniority step away in the same broad occupation family. They can reveal nearby options, but they are not a guaranteed promotion path and do not account for your personal experience.

Skill bridge

What does this role connect to?

Python is one of the signals this listing shares with skills appearing often across current Data & AI roles opportunities.

Skills named in this listing

PythonSQL

Shared with the current career sample

  • Python Seen in 88 sampled listings
  • SQL Seen in 76 sampled listings

Combinations also appearing together

  • Python + SQL appeared together in 64 sampled listings

Application edge

  • If these skills match your background, prepare one concrete example that shows how you used Python and SQL rather than relying on a keyword-only claim.
  • The listing mentions extra application steps. Review those requirements before starting so you can prepare the requested material in one pass.

If the named skills are genuinely transferable in your own experience, the occupation map also points to adjacent current families worth researching: Software engineering roles and Product roles. This is exploration context, not a qualification match.

Skill signals come from explicit text mentions. A mention is not automatically a requirement, and absence from the current top-skill list does not make a skill rare or unimportant. The current career comparison draws on 120 sampled listings; 115 contain a recognized skill signal.

Application Decision Lab

Turn the listing into a preparation plan

Separate what the listing actually states from what you still need to prepare or verify. Your selections and short notes stay in this browser and are never submitted to High Paid Job.

Listing mention

Python

This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.

Listing mention

SQL

This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.

Application step

Prepare for a stated application step

An assessment, test or work-sample step is explicitly mentioned.

Application step

Prepare for a stated application step

Professional references are explicitly mentioned.

Application step

Prepare for a stated application step

A case study, presentation or case-style exercise is explicitly mentioned.

Questions worth resolving before you rely on the opportunity

  • Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
  • Work authorization / sponsorship: If sponsorship or work authorization matters to you, what exact immigration or employer-sponsorship conditions apply to this role?

Decision analysis

What the listing really tells you

Useful decision context

A closer look at Senior Data Scientist, Full Stack at TRM Labs points most strongly to the advertised pay can be placed in context, the location rules are clear enough to judge, and the application route gives a few useful clues. Those signals shape the practical upside and the questions still worth asking.

The advertised pay can be placed in context

The annualized midpoint sits near the 56th percentile within 52 comparable USD salary observations. Treat that as current advertised-pay context rather than a forecast of take-home or total compensation.

The location rules are clear enough to judge

Location flexibility can be assessed here: Limited location freedom, with a Remote Freedom reading of 56/100. The most important location constraint is geographic eligibility is restricted to a country or region..

The application route gives a few useful clues

Provider-mediated application path · 3 visible process steps. The external application may contain additional steps that are not visible from the listing itself.

The listing can be traced to a current source

The opportunity currently has 1 renewable source path across 1 source classes. Multiple independent paths can strengthen continuity, but they are not a substitute for checking the application destination.

How this analysis is assembled

The brief pulls together the parts of the vacancy that can be compared reliably today. Gaps in the listing remain visible instead of being filled with assumptions.

Analysis evidence coverage

How much analysis can the current evidence support?

100%Strong analysis evidence coverage
100%
Current-sample pay position56 / 100

Uses the explainable High Pay Score within the job’s current occupation/seniority/currency comparison sample.

Remote Freedom evidence56 / 100

Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.

Salary evidence qualityHigh confidence · Focused salary range

The configured source supplied structured salary fields, a usable currency and a focused comparison value.

Listing freshnessVery fresh

The listing was posted about 1 days ago.

Source provenance evidenceEstablished source evidence

At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.

There is no universal opportunity score because pay, flexibility and access matter differently to different people. Compare Jobs lets you weight the evidence around your own priorities.

Decision checklist

What is supported, uncertain or worth checking?

This checklist separates supported strengths, visible cautions and missing evidence. It is not an overall recommendation or guarantee that the job is suitable for you.

Supported strengths

No standout strength is supported strongly enough to list here. That is not a negative rating.

Visible cautions

  • Geographic eligibility is restricted to a country or region.
  • Specific timezone availability or overlap appears to be required.
  • The role may involve non-standard hours or on-call work.
  • This is remote work with a geographic restriction, not a worldwide-remote vacancy.

Still unknown

  • Visa sponsorship is not established by the available listing data.

Qualifications & access

What qualifications are explicitly stated?

Entry-barrier evidence limited

EducationEducation requirement not established
Required experience evidenceExperience requirement not established
Explicit required barriers0
Explicit access signals0

Qualification signals come from explicit listing wording. Anything not mentioned remains unknown, so silence is never treated as proof that a degree, experience, licence or other requirement is unnecessary.

Pay vs. barriers

Pay and barrier context available

The current pay position can be viewed alongside the stated barriers, but the available detail does not support a stronger conclusion.

Current-sample pay position

Comparable disclosed-pay evidence places this job at about 56 / 100 within its current occupation/seniority sample.

Location-freedom constraints

2 explicit Remote Freedom restriction signals are present and are shown separately from qualification requirements.

Evidence gaps that could change this interpretation
  • Education requirement is not established.
  • Required prior-experience level is not established.

This section weighs pay context against clearly stated barriers without collapsing unlike requirements into one universal score. Degree, experience, licensing, clearance and location limits remain separate, and missing qualification details stay unknown.

Beyond the headline

Not enough evidence for a hidden-value finding

Potential hidden value appears only when several distinct non-pay advantages are actually supported. Missing requirements or benefits are never counted as positives.

Competitive current-sample pay position

The annualized salary midpoint is at or above about 56% of the current comparable disclosed-salary sample.

Still check: The current application path is provider-mediated rather than directly classified as an employer-authorized or official route.

Hidden Value is an experimental evidence summary, not an investment-style score or guarantee of employability. It only uses explicit current listing evidence and does not infer that an unmentioned qualification is unnecessary.

Explainable opportunity highlight

Why this opportunity stands out

These are supported signals from the current listing and comparison sample—not a guarantee that this is the best job for you.

Clear compensation evidence

The source provides structured salary information with a comparatively focused range, improving the usefulness of pay comparisons.

Fresh opportunity

The listing was posted about 1 days ago.

Compensation intelligence

How competitive is this pay?

56/100

Within the current market range

56%
Comparison familyData & AI roles · Senior / lead
Comparable salaries52
Current-sample medianUSD 187,500
Middle 50% of sampleUSD 163,750–225,000
Position in sampleAt or above about 56%
Comparison geographySame country (US)
Pay positionAbove the current median

The annualized midpoint is above the current comparable median but remains within the sample’s middle-to-upper range.

Why this analysis?

  • This score reflects the current advertised-pay distribution for comparable active jobs in the same currency. It measures pay position, not overall job quality.
  • Salary confidence: High. The configured source supplied structured salary fields, a usable currency and a focused comparison value.
  • Range quality: Focused salary range.

Employer compensation communication

Pay transparency at TRM Labs

Mixed current pay transparency

This score uses the employer’s current visible listings and combines how often pay is disclosed with how useful those ranges are. Bonus, equity and benefit mentions are shown separately and do not raise the score.

48/100
48%
Salary disclosure35% · 18 of 52 sampled listings
Range qualityMostly focused or specific pay information
Bonus wording4 current listings
Equity wording11 current listings
Benefits wording1 current listings
Evidence strengthStronger

This pattern comes from the employer’s current visible vacancies. It describes hiring and pay transparency in those listings, not employee satisfaction or a permanent judgement of the employer.

Current employer intelligence

TRM Labs — current opportunity snapshot

Current opportunities visible52
Salary disclosure35%
Remote-policy evidence coverage100% · 52 of 52 sampled listings
Remote share of known arrangements83%
Worldwide-remote share2%
Known hiring countries9
Sponsorship evidence coverage0% · 0 of 52 sampled listings
Explicit sponsorship outcomes0 confirmed · 0 possible · 0 unavailable
Pattern sample52 of 52 · 100% coverage
Current role-family breadth7
Stated closing-date coverage2%
Relocation evidence coverage0% · 0 explicitly offered
Posted within 7 days29% of listings with a known posting date

Employer Evidence Profile

How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.

Compensation35% of sampled current listings

Current sampled listings with advertised compensation evidence.

Work arrangement100% of sampled current listings

Current sampled listings whose work arrangement is clearer than unknown.

Location71% of sampled current listings

Current sampled listings with a supported country signal.

Application timing2% of sampled current listings

Current sampled listings with an explicit closing time.

Posting date100% of sampled current listings

Current sampled listings with a usable posting date.

Sponsorship0% of sampled current listings

Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.

Relocation0% of sampled current listings

Current sampled listings with an explicit relocation signal.

Low coverage means the current visible listings state the information less often. It does not prove that the employer lacks the policy, benefit or support.

Common current occupation families: Executive (11) · Software engineering (6) · Sales (5)

Visible seniority mix: Senior / lead (25) · Leadership (22)

Current employment-type mix: Full time (49) · Contract (3)

Most visible hiring countries in this sample: United States (17) · Brazil (6) · United Kingdom (5) · Singapore (3)

Other current roles from this employer

These are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.

Current disclosed salary medians by currency

  • USD 164,500 median · 18 disclosed salaries

Questions worth asking this employer

  • What compensation range applies to this role, and which parts of total compensation are fixed versus variable?
  • If work authorization matters to you, what sponsorship policy applies to this specific role and location?
  • If a move is required, is relocation assistance available for this position?
  • What is the expected application and interview timeline if the listing does not state a closing date?

These questions are prompted by missing or thin evidence in the employer’s current visible vacancies. Missing evidence is not treated as a negative judgement.

This employer snapshot uses the employer’s current visible listings. Remote and sponsorship rates use only vacancies with explicit evidence, while missing information stays unknown. Salary medians are shown separately by currency and need at least five disclosed salaries. Zero confirmed sponsorship means none was explicitly found in this sample, not that sponsorship is unavailable.

This snapshot describes the employer’s current visible vacancies—things such as pay disclosure and work arrangement. It is not an employee-satisfaction or hiring-quality rating, and different currencies stay separate.

Work-location intelligence

Remote Freedom

56/100

Limited location freedom

56%

Why this score?

  • The role is remote but the source identifies a country or regional restriction.

Location restrictions detected

  • Geographic eligibility is restricted to a country or region.
  • Specific timezone availability or overlap appears to be required.

Flexibility signals detected

  • The description reinforces broad location independence.

Remote Freedom measures observable location flexibility only. Unknown work-location information remains unscored. Timezone wording is penalized only when it appears restrictive; general collaboration across time zones is not treated as a restriction. A score does not guarantee legal work eligibility, payroll availability or visa sponsorship.

International applicant intelligence

Visa & relocation signals

Sponsorship status is shown only when the listing provides clear enough wording. Employer history or headquarters location is not used to guess an answer.

Visa sponsorshipSponsorship not established

The available listing details do not contain a clear enough sponsorship statement.

RelocationRelocation support not established

The available listing details do not contain a clear enough relocation statement.

Tradeoff check

What’s the catch?

These are visible conditions worth checking before treating the headline compensation as the whole story:

  • The role may involve non-standard hours or on-call work.
  • This is remote work with a geographic restriction, not a worldwide-remote vacancy.

Current alternatives

Better-paying alternatives

These current roles show a materially higher annualized salary midpoint in the same broad occupation and currency. Similar seniority, country, work arrangement, employment type and recency are preferred where possible; higher pay alone does not make an alternative a better fit.

About +24% midpoint

Lead Machine Learning Engineer

Serve Robotics · Canada, USA

USD 225,000–260,000 / year Region-restricted remote

Why comparable: Same seniority band · Same country · Similar work-location arrangement

Alternatives are drawn from the current inventory rather than presented as application recommendations. A pay difference needs to be meaningful—about 3% or more at the midpoint—and different currencies are not compared.

Application-path intelligence

What can we see about the application process?

Provider-mediated application path

The current application path comes through a publisher or aggregator. The destination may still lead to an employer-authorized hiring system, but that is not clear enough to assume.

Current destination: jobicy.com · HTTPS

Visible application steps or requirements

  • An assessment, test or work-sample step is explicitly mentioned.
  • Professional references are explicitly mentioned.
  • A case study, presentation or case-style exercise is explicitly mentioned.

Application effort is estimated from visible listing and source details. Optional steps are kept separate from mandatory ones, and the external form may contain additional requirements that are not visible here.

Application safety context

Application safety check

Basic application-safety context available

The current application destination has a basic transport-safety signal such as HTTPS. This does not verify the employer, vacancy, recruiter or application process.

Evidence checked: Source provenance · Application destination · Listing wording

Observable transport-safety signals

  • The available application destination uses HTTPS.
Before you share money or sensitive information
  • Confirm the vacancy on the employer’s official careers site when practical, especially if a recruiter contacts you outside the listed application path.
  • Check that recruiter email domains and application destinations are consistent with the employer or an identifiable recruiting provider.
  • Do not pay application, processing, training or equipment fees merely to be considered for a job.
  • Do not send cryptocurrency, gift cards, online-banking credentials or PINs as part of an application.
  • Treat unusually fast offers or interviews conducted only through informal messaging channels as reasons to verify the employer independently.

This is a limited safety screen, not employer verification and not a fraud determination. Never send money, cryptocurrency, passwords or sensitive banking credentials merely because a listing appears on a job platform.

Original listing details

The section below comes from the listing source. The decision sections above use the job’s structured pay, location, qualification, mobility and application details to give you additional context.

Build a Safer World.

TRM Labs provides AI-powered intelligence solutions that help public and private sector agencies investigate and disrupt crime. TRM's platforms enable investigators to trace illicit activity, build cases, and construct operating pictures of threat networks. Leading agencies and businesses worldwide rely on TRM to make the world safer and more secure.

TRM Labs is a blockchain intelligence company committed to fighting crime and creating a safer world. By leveraging blockchain data, threat intelligence, and advanced analytics, our products empower governments, financial institutions, and crypto businesses to combat illicit activity and global security threats. At TRM, you'll join a mission-driven, fast-paced team made up of experts in law enforcement, data science, engineering, and financial intelligence, tackling complex global challenges daily.

As a Senior Data Scientist, Attribution, you will be embedded within a specific risk category (e.g., Sanctions, Cybercrime, Counter Terrorism) and own the attribution algorithms that drive direct customer outcomes — from seizing illicit funds to disrupting crypto-enabled crime networks. You'll operate as equal parts Subject Matter Expert and Data Scientist, translating deep domain knowledge into production-grade intelligence.

The impact you'll have here:

  • Own end-to-end attribution for a risk category: identify high-impact targets, build algorithms (heuristics, ML models, AI agents), and ship to production.

  • Collaborate tightly with — and become — a Subject Matter Expert for your category, translating customer and GTM needs into actionable intelligence.

  • Navigate all intelligence layers (Network / On-chain / Protocol) to uncover patterns competitors can't replicate.

  • Ship production-quality code — heuristics, ML models, and data pipelines — with rigorous evaluation and scalable architecture.

  • Drive measurable outcomes: billions in illicit funds traced, seized, or prevented.

What we’re looking for:

  • Quantitative background (Computer Science, Statistics, Engineering, Economics, Physics).

  • Strong problem-solving mindset: gets to the root cause, leverages creativity, and thrives in ambiguity with a hypothesis-driven, iterative approach.

  • Demonstrated domain curiosity — ability to absorb and apply expertise in blockchain, fraud, money laundering, or cybercrime.

  • 6+ years in an analytical role with strong SQL, Python, and AI-assisted coding tools (Cursor, Claude Code).

  • Experience with distributed data systems (Spark, BigQuery, Airflow) and shipping production code.

  • Strong async communication (Loom, Slack, Notion) and ability to own work independently in a remote environment.

  • Bonus: Deep ML experience, prior work in crypto, financial crime, or financial institutions.

About the Team:

  • Data Scientists are embedded within risk categories teams (Sanctions, Cybercrime, Nation States, etc.), working closely with SMEs, engineers, and cross-functional partners.

  • Fully remote and globally distributed. Most overlap occurs 7am–12pm PST for meetings and collaboration.

  • All team members must have at least 4-hour overlap with PST business hours.

  • On-call responsibilities: Analytics on-call (< 1 hr/day monthly) and Data Science on-call (1-2 hrs/day every 3 months).

Learn about TRM Speed in this position:

  • Rapid Customer Impact. Deliver last-minute, high-stakes projects for key customers — turning around attribution insights in days, not weeks.

  • MVP to Production. Build complex algorithms with tight timeframes (1-2 sprints for MVP) and iterate based on real customer feedback.

  • Parallel Execution. Juggle multiple priorities across categories and stakeholders while maintaining quality and speed.

About TRM's Engineering Levels:

Engineer: Responsible for helping to define project milestones and executing small decision decisions independently with the appropriate tradeoffs between simplicity, readability, and performance. Provides mentorship to junior engineers, and enhances operational excellence through tech debt reduction and knowledge sharing.

Senior Engineer: Successfully designs and documents system improvements and features for an OKR/project from the ground up. Consistently delivers efficient and reusable systems, optimizes team throughput with appropriate tradeoffs, mentors team members, and enhances cross-team collaboration through documentation and knowledge sharing.

Staff Engineer: Drives scoping and execution of one or more OKRs/projects that impact multiple teams. Partners with stakeholders to set the team vision and technical roadmaps for one or more products. Is a role model and mentor to the entire engineering organization. Ensures system health and quality with operational reviews, testing strategies, and monitoring rigor.

The following represents the expected range of compensation for this role:

  • Individual pay is determined by skills, qualifications, experience, and location. The compensation details listed in this posting reflect the US base salary only.

  • The estimated base salary range for this role is $180,000 - $210,000

  • Additionally, this role may be eligible to participate in TRM’s equity plan.

  • Please note – we factor in the different costs for geographies outside the United States.

Application Instructions

If you’re interested in joining TRM, we encourage you to apply directly. Every application is reviewed by our Talent team.

Before applying, review the job description carefully and highlight the experience and impact that best demonstrate the required qualifications. Please also provide thoughtful and accurate answers to the application questions, as these will be used to evaluate your qualifications for the role.

If you send your resume directly to someone at TRM, we can’t guarantee it will reach the appropriate hiring team. Applying directly is the best way to ensure you’re considered.

What to Expect From Our Interview Process

Our process is designed to understand how you think, solve problems, and deliver impact, while giving you the opportunity to evaluate TRM. Most interview processes include a case study, AI skills assessment, and Leadership Principles interview.

  • Recruiter Intro: Explore your experience, motivations, and alignment with the role.

  • Hiring Manager: Dive deeper into your relevant experience, skills, and impact.

  • First Round: Typically 1–2 interviews focused on the skills most critical to the role.

  • Final Round: Typically 3–5 interviews to go deeper on your craft, problem-solving, and alignment with TRM.

  • References: We’ll speak with former colleagues who can provide perspective on your work and impact.

  • Offer: If it’s a mutual fit, your recruiter will walk you through your offer and answer your questions.

  • Welcome to TRM: Once you sign, we’ll get you ready for your first day and onboarding.

Your recruiter will share your specific interview plan and preparation guidance along the way.

Learn more about interviewing at TRM

Life at TRM

We are building a safer world. That promise shows up in how we work every day.

TRM moves quickly. We are a high velocity, high ownership team that expects clarity, follow-through, and impact. People who thrive here are energized by hard problems, experimentation, and continuous feedback. If something takes months elsewhere, it will ship here in days.

Our work sits at the intersection of AI, national security, and fighting crime. The problems are complex, the stakes are real, and the environment evolves quickly. The pace and intensity of the work reflect the importance of the mission. As a result, the way we operate requires a high level of ownership, adaptability, collaboration, and creative problem-solving.

At TRM, you should expect:

  • Priorities and targets to change quickly as we experiment and iterate

  • Work that often requires operating with a high degree of ambiguity

  • A high level of personal ownership and accountability

  • Close collaboration across teams and functions

  • Frequent, high-touch communication

  • Creative problem solving and out-of-the-box thinking

  • A pace that rewards urgency, adaptability, and outcomes

This environment is energizing for people who enjoy building, solving hard problems, and making progress in situations that are not always fully defined. It also requires comfort navigating ambiguity, adjusting course as new information emerges, and maintaining focus and positivity in a fast-moving and intense environment.

We also recognize that this style of operating is not for everyone. If you are primarily optimizing for predictability or a consistently balanced workload, we encourage you to use the interview process to pressure test whether this environment is truly the right fit. We want teammates who thrive here, not just survive here.

At the same time, many people find this work deeply rewarding. If you are excited by meaningful problems, motivated by ambitious goals, and energized by working alongside mission-driven colleagues, there is a good chance you will find TRM to be an exceptional place to grow and contribute. Learn more about Interviewing at TRM

AI Fluency at TRM

AI fluency is a baseline expectation at TRM.

We believe AI meaningfully changes how top performers operate. We expect every team member to use AI to accelerate and reimagine their craft, not just automate surface tasks.

At TRM, AI fluency means you are among the top 10 percent of operators in your function in how you apply AI to:

  • Accelerate repeatable workflows

  • Structure and solve problems

  • Improve output quality

  • Increase speed and leverage

You will be evaluated on applied AI fluency during the interview process.

Leadership Principles

We hire and grow against three leadership principles. They’re the standards for how we operate, treat each other, and make decisions.

  • Impact-Oriented Trailblazer: We put customers first and move with speed, focus, and adaptability. We treat every plan like an experiment – test, ship, measure, and iterate quickly.

  • Master Craftsperson: We care deeply about our craft. We balance speed with high standards, own outcomes end‑to‑end, and invest in getting better everyday.

  • Inspiring Colleague: We add clarity and energy, not noise. We bring humility, candor, and a one‑team mindset — giving and receiving feedback to make the team stronger.

Join our Mission

At TRM, we care deeply about our craft. We are looking for individuals who want their work to matter, who experiment with speed and rigor, and who take pride in building a safer world for billions of people. If you’re excited by TRM’s mission but don’t check every box, we encourage you to apply — we hire for slope, judgment, and the will to learn fast.

TRM is a Series C company with $220M in total funding, backed by Goldman Sachs, Bessemer, Y Combinator, Thoma Bravo, and others. Headquartered in San Francisco, TRM operates as a distributed-first company with hubs in Los Angeles, San Francisco, New York, Washington D.C., London, and Singapore. Learn more about building tools for defenders.

Privacy Policy and Additional Information

By submitting your application, you agree to allow TRM Labs to process your personal information in accordance with our Privacy Policy.

We collect the information you provide (such as your resume, work history, and contact details) solely for the purpose of evaluating your candidacy for current and future roles at TRM.

Because our hiring cycles for certain positions may span 24 to 36 months, we retain your personal information for up to 36 months from the date of your application. After that period, your data is deleted unless a different retention period is required or permitted by law.

If you are located in the European Economic Area, the United Kingdom, or another jurisdiction with applicable data protection laws, you have the right to access, correct, or request deletion of your personal data at any time before that period ends. To exercise any of these rights, contact us at privacy@trmlabs.com.

To notify TRM Labs that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.

The use of AI tools of any kind (including but not limited to notetakers, interview assistants, and real-time coaching tools such as Otter.ai, Fireflies, Fathom, Cluey, or similar) during TRM interviews is not permitted without prior approval from TRM. TRM uses its own internal tools for note-taking to ensure a consistent and confidential experience for all candidates.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this form.

Recruitment agencies

TRM Labs does not accept unsolicited agency resumes. Please do not forward resumes to TRM employees. TRM Labs is not responsible for any fees related to unsolicited resumes and will not pay fees to any third-party agency or company without a signed agreement.

Learn More: Company Values | Interviewing | FAQs

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