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Job intelligence

Data Scientist, Corporate

YipitData

Location: USA Advertised pay: USD 170,000 / yearly Work arrangement: Region-restricted remote Employment: Full Time Posted: October 8, 2026 Freshness: Active Source check: Observed in the current source window

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 positionBelow the current median39 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 alternativesCurrent role context limitedCurrent 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.
Watch closely: Application closing time

Fast read

Opportunity DNA

This opportunity needs a closer read before drawing a strong conclusion, especially around location rules could change the fit.

PayRange available

Current comparable position is around the 33th percentile.

FlexibilityLocation limits visible

Geographic eligibility is restricted to a country or region.

Entry pathMixed requirements

Some access and qualification signals are clear, but the overall entry path is neither unusually open nor unusually restrictive.

MobilityNot established

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

ApplicationSome process detail

Part of the application route can be assessed, but the full external process may contain additional steps.

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.

Questions worth asking before you invest more time

  • 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.

Principal Data Analyst

Tendo · Remote — Worldwide

USD 110,500–149,500 / year Worldwide remote 5/5 core facts

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

About 24% lower advertised midpoint

Analytics Engineer

OnTrac · Remote — Worldwide

USD 104,800–157,200 / year Worldwide remote 5/5 core facts

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

About 23% lower advertised midpoint

Senior Staff Data Scientist

Netlify · Anywhere in the World

Salary not disclosed Worldwide remote 4/5 core facts

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

Senior Data Scientist with German / SO

Inetum · Remote — Worldwide

Salary not disclosed Worldwide remote 4/5 core facts

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

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 roles891

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

Employers represented152

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

Decision-ready disclosure27%

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

Compensation stated16%

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

Work arrangement stated96%

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

Stated closing time19%

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 (20) · GBP (4) · EUR (4) · CAD (2). 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 23 / 200 12%

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

Require usable compensation disclosure 31 / 200 16%

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

Require at least four Core Decision Facts 53 / 200 27%

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

Stay in the same stated country 21 / 77 27%

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

Keep this advertised salary floor in the same currency 6 / 20 30%

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

Require a remote-listed arrangement 62 / 200 31%

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

Keep the same employment type 105 / 122 86%

Uses only roles with a stated employment type.

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 891 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 16% 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 sample16%

Pay disclosure is uncommon in the current role sample.

Current employer pattern100%

This employer’s current visible listings disclose pay materially more often than 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 170,000 / yearListing evidence only.
Current comparable medianUSD 173,83420 current sampled roles
Current upper quartileUSD 186,250Current 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 / pensionMentioned

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

Health benefitsVerify

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 health coverage is included and what costs remain with the employee?
  • 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 engineeringPhrase in listing
  • 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
  • MathematicsPhrase in listing
  • 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 · Exact phrase in listing
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 · Exact phrase in listing
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

Software engineering roles

DevOps Engineer

Skatteverket · Göteborg, Västra Götalands län, Sverige

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 Engineer Analyst

Blend360 · Remote — Worldwide

Similar work arrangement · Same employment type · Recently posted
Mid-career

Data Engineer

DIGITALL Nature Corp · Remote — Worldwide

Similar work arrangement · Same employment type · Recently posted

Stretch direction

Leadership

Sr. Data Engineer

Carrot · USA

USD 145,000–165,000 / year Same country · Similar work arrangement · Same employment type Advertised midpoint is about 9% 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?

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

Skills named in this listing

DatabricksLarge Language ModelsPythonSQL

Shared with the current career sample

  • Databricks Seen in 20 sampled listings
  • Large Language Models Seen in 39 sampled listings
  • Python Seen in 89 sampled listings
  • SQL Seen in 70 sampled listings

Combinations also appearing together

  • Python + SQL appeared together in 60 sampled listings
  • Large Language Models + Python appeared together in 31 sampled listings

Application edge

  • If these skills match your background, prepare one concrete example that shows how you used Databricks, Large Language Models, and Python rather than relying on a keyword-only claim.

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; 117 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.

Education evidence

Degree preference stated

The listing describes a degree as preferred rather than clearly mandatory. Prepare the strongest relevant evidence you have, and verify whether equivalent experience is acceptable.

Listing mention

Databricks

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

Large Language Models

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

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.

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?
  • Application process: What interview, assessment, portfolio, reference or screening stages are part of the hiring process?

Decision analysis

What the listing really tells you

Rich decision context

The headline alone does not tell the whole story for Data Scientist, Corporate at YipitData. The clearest decision context comes from compensation has enough depth for comparison, the location rules are clear enough to judge, and qualifications reveal more than the job title alone.

Compensation has enough depth for comparison

Among 39 current comparable salaries in USD, the normalized annual midpoint is around the 33th percentile. This compares advertised pay in the same currency and does not include every element of total compensation.

The location rules are clear enough to judge

Location flexibility can be assessed here: Remote with some uncertainty or restrictions, with a Remote Freedom reading of 62/100. One practical limit to notice is geographic eligibility is restricted to a country or region..

Qualifications reveal more than the job title alone

Degree preference stated. Unstated education or experience requirements stay open rather than being assumed.

The application route gives a few useful clues

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

Source continuity adds useful context

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

This read combines the structured details currently available for pay, flexibility, qualifications, mobility, application effort and source continuity. Missing facts are left open for you to verify.

Analysis evidence coverage

How much analysis can the current evidence support?

100%Strong analysis evidence coverage
100%
Current-sample pay position33 / 100

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

Remote Freedom evidence62 / 100

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

Salary evidence qualityHigh confidence · Single disclosed value

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

Listing freshnessActive

The listing was posted about 13 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.
  • Some compensation may be variable or performance-based. Confirm the guaranteed base amount before comparing headline pay.
  • The description appears to mention an advanced degree requirement or preference.

Still unknown

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

Qualifications & access

What qualifications are explicitly stated?

Entry-barrier evidence limited

EducationDegree preference stated
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 33 / 100 within its current occupation/seniority sample.

Location-freedom constraints

1 explicit Remote Freedom restriction signal is present and is shown separately from qualification requirements.

Evidence gaps that could change this interpretation
  • 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.

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.

Compensation intelligence

How competitive is this pay?

33/100

Below many comparable disclosed salaries

33%
Comparison familyData & AI roles · Senior / lead
Comparable salaries39
Current-sample medianUSD 190,000
Middle 50% of sampleUSD 149,720–225,000
Position in sampleAt or above about 33%
Comparison geographySame country (US)
Pay positionBelow the current median

The annualized midpoint is below the current comparable median but remains within the sample’s lower-to-middle 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: Single disclosed value.

Employer compensation communication

Pay transparency at YipitData

Very strong 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.

98/100
98%
Salary disclosure100% · 15 of 15 sampled listings
Range qualityMostly focused or specific pay information
Bonus wording11 current listings
Equity wording7 current listings
Benefits wording15 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

YipitData — current opportunity snapshot

Current opportunities visible15
Salary disclosure100%
Remote-policy evidence coverage100% · 15 of 15 sampled listings
Remote share of known arrangements100%
Worldwide-remote share0%
Known hiring countries1
Sponsorship evidence coverage0% · 0 of 15 sampled listings
Explicit sponsorship outcomes0 confirmed · 0 possible · 0 unavailable
Pattern sample15 of 15 · 100% coverage
Current role-family breadth6
Stated closing-date coverage0%
Relocation evidence coverage0% · 0 explicitly offered
Posted within 7 days13% 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.

Compensation100% 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.

Location100% of sampled current listings

Current sampled listings with a supported country signal.

Application timing0% 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: Product (3) · Software engineering (2) · Executive (1)

Visible seniority mix: Senior / lead (9) · Leadership (5)

Current employment-type mix: Full time (15)

Most visible hiring countries in this sample: United States (15)

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 150,000 median · 15 disclosed salaries

Questions worth asking this employer

  • 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

62/100

Remote with some uncertainty or restrictions

62%

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.

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:

  • Some compensation may be variable or performance-based. Confirm the guaranteed base amount before comparing headline pay.
  • The description appears to mention an advanced degree requirement or preference.
  • 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 +49% midpoint

Sr. Staff Machine Learning Systems Engineer

hims & hers · USA

USD 240,000–265,000 / 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

No additional required application step is clear in the available listing text. This does not mean the external application is one-click or friction-free.

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.

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and most recently raised $475M from The Carlyle Group at a valuation of over $1B. Every day, our proprietary technology analyzes billions of alternative data points to uncover actionable insights across sectors like software, AI, cloud, e-commerce, ridesharing, and payments.

Our data and research teams transform raw data into strategic intelligence, delivering accurate, timely, and deeply contextualized analysis that our customers—ranging from the world’s top investment funds to Fortune 500 companies—depend on to drive high-stakes decisions. From sourcing and licensing novel datasets to rigorous analysis and expert narrative framing, our teams ensure clients get not just data, but clarity and confidence.

We operate globally with offices in the US, APAC, and India. Our award-winning, people-centric culture—recognized by Inc. as a Best Workplace for three consecutive years—emphasizes transparency, ownership, and continuous mastery.

What It’s Like to Work at YipitData:

YipitData isn’t a place for coasting—it’s a launchpad for ambitious, impact-driven professionals.

From day one, you’ll take the lead on meaningful work, accelerate your growth, and gain exposure that shapes careers.

Why Top Talent Chooses YipitData:

  • Ownership That Matters: You’ll lead high-impact projects with real business outcomes
  • Rapid Growth: We compress years of learning into months
  • Merit Over Titles: Trust and responsibility are earned through execution, not tenure
  • Velocity with Purpose: We move fast, support each other, and aim high—always with purpose and intention

If your ambition is matched by your work ethic—and you're hungry for a place where growth, impact, and ownership are the norm—YipitData might be the opportunity you’ve been waiting for.

About The Role:

We are looking for a Data Scientist to join our core Panel Science team. Our team is dedicated to building an uncompromising, mathematically rigorous single source of truth for enterprise consumer analytics.

In this role, you will work across a highly sophisticated data engine that bridges foundational statistical modeling, complex data infrastructure, and advanced analytical product architecture. Whether your deep expertise lies in refining core statistical baselines, optimizing heavy computational models, or architecting scalable logic libraries, you will have a massive impact on how enterprise-level consumer insights are delivered.

This is a remote-friendly opportunity that can sit in NYC (where our headquarters is located), one of our office hubs, or anywhere else in the US. However, depending upon where the remote work is performed, income could be subject to New York State tax withholding. We expect East Coast work hours.

As Our Corporate Data Scientist, You Will:

  • Statistical Modeling & Distribution Optimization: Design, validate, and deploy robust, scalable optimization frameworks and distribution models to ensure complete mathematical integrity across downstream metrics.
  • Methodology & Panel Foundations: Explore and execute complex methodologies, evaluate methodological trade-offs, and implement advanced statistical procedures.
  • Analytical Logic & Governance: Partner cross-functionally to transition ad hoc code into standardized, version-controlled templates and logical libraries. You will ensure analytics are structured cleanly to power diverse product surfaces, ranging from interactive dashboards to LLM-driven applications.
  • Infrastructure & Performance Optimization: Partner closely with Data Engineering to optimize code performance and scale analytical workflows. You will help transition complex statistical calculations from standard database queries into efficient, production-grade cloud compute architectures.

You Are Likely To Succeed If:

  • Experience: 4–5 years of professional data science experience. 2-3 years of experience with an advanced degree.
  • Education: Bachelor’s degree in a highly quantitative field (Statistics, Economics, Physics, Mathematics, Engineering, or a related discipline). A Master’s degree is strongly preferred.
  • Mathematical Proficiency: Exceptional expertise in working with, modeling, and optimizing for statistical distributions.
  • Technical Stack: Advanced proficiency in Python or R and expert-level SQL.
  • Key Advantages (Big Plus):
    • Direct experience working with panel data or longitudinal consumer data.
    • Hands-on experience analyzing receipt data or transaction-level consumer data.
    • Experience working with Databricks and scaling advanced compute workloads.

What We Offer:

Our compensation package includes comprehensive benefits, perks, and a competitive salary:

  • We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
  • Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
  • The annual on-target earnings for this position are anticipated to be up to $170K + bonus element. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks.
  • The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have office hubs in Austin, Miami, Denver, Mountain View, and Seattle. If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

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