Job intelligence
Staff Data Scientist
hims & hers
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.
Fast read
Opportunity DNA
This opportunity needs a closer read before drawing a strong conclusion, especially around the salary benchmark is still shallow.
The range is usable, but the comparable sample is still too shallow for a strong relative-pay call.
Geographic eligibility is restricted to a country or region.
One or more material qualification requirements are stated and may narrow the realistic applicant pool.
Sponsorship and relocation support are not clear from the current listing.
Part of the application route can be assessed, but the full external process may contain additional steps.
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
- Advertised location
- Employment type
What to verify next
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
Advertised location is not proof of work authorization, sponsorship, or legal eligibility. Unknown information is not a negative score; verify those questions separately.
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
- The salary benchmark is still shallow: Pay is disclosed, but the current comparison pool is too small for a strong relative-pay conclusion.
- Location rules could change the fit: Geographic eligibility is restricted to a country or region.
- The experience requirement raises the entry bar: The listing appears to require at least about 8 years of experience.
Questions worth asking before you invest more time
- Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
- 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.
Data Scientist
Solace · USA
Why it is nearby: Same seniority band · Same country · Similar work arrangement
About 14% lower advertised midpoint
Commercial Data Scientist
Doximity · USA
Why it is nearby: Same country · Similar work arrangement · Same employment type
About 30% lower advertised midpoint
Different angle: Seniority is not explicit
PERFORMANCE AND SPORT DATA ANALYST NF4
U.S. Marine Corps · Twentynine Palms, California
Why it is nearby: Same country · Recently posted
About 57% lower advertised midpoint
Different angle: Seniority is not explicit
Data Scientist
Occupational Safety and Health Administration · Washington, District of Columbia
Why it is nearby: Same country · Recently posted
About 21% lower advertised midpoint
Different angle: Seniority is not explicit
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.
200 most recent current roles sampled for the disclosure measures below.
Distinct employers in the bounded current sample, not an estimate of all employers hiring in the market.
Current sampled roles stating at least four of the five Core Decision Facts.
Requires a numeric amount, explicit ISO currency and supported pay period.
Listings with explicit enough remote, hybrid or on-site evidence to classify.
Missing deadline evidence remains unknown and is not interpreted as an open-ended application window.
Currencies with explicit pay evidence in the sample: USD (43) · EUR (8) · GBP (3) · CHF (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.
Counts only affirmative or possible sponsorship evidence, or relocation explicitly offered. Unknown is not treated as no.
A much stricter subset: only listings with explicit worldwide-remote evidence remain.
Only same-currency opportunities with usable annualized pay enter this comparison. Other currencies are not converted or treated as lower.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
Shows the subset with enough stated evidence for a more informed first comparison.
Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.
Includes roles whose seniority is genuinely unspecified rather than assuming they belong to another level.
Uses only roles where a country is known. Roles without country evidence are excluded from this denominator.
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 1226 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 27% of the bounded current role sample does the same.
Compensation counts only when amount, ISO currency and supported pay period are all known.
Pay disclosure is limited in the current role sample.
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.
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.
Usable advertised compensation is available.
Variable compensation is mentioned; verify amount, formula, target and eligibility.
Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.
Retirement or pension benefits are mentioned; verify employer contribution and vesting.
Health-benefit language is mentioned; verify coverage, dependants and employee cost.
Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.
A work-arrangement signal is available; verify schedule details before relying on it.
Relocation support not established
Sponsorship not established
A review cycle is mentioned; verify timing and whether compensation can change at that review.
Questions worth taking into the offer conversation
- Is there a bonus, commission or other variable-pay component, and how is it calculated?
- 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.
European occupation references
This service uses the ESCO classification of the European Commission.
U.S. occupation references
- Data Scientists 15-2051.00
- Computer and Information Research Scientists 15-1221.00
- Social Science Research Assistants 19-4061.00
- Atmospheric and Space Scientists 19-2021.00
- Bioinformatics Scientists 19-1029.01
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
- statisticsPhrase in listing
- think abstractly
- apply research ethics and scientific integrity principles in research activities
- integrate gender dimension in research
- promote the participation of citizens in scientific and research activities
- communicate with a non-scientific audience
- implement data quality processes
Context-dependent ESCO skills
- Hadoop
- unstructured data
- apply blended learning
- teach in academic or vocational contexts
- digital curation
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.
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
Staff Software Engineer, Backend
Customer.io · LATAM, Canada, USA
same stated country · same seniority band · same employment typeProduct Lead, CodePath EDU
CodePath · USA
same stated country · same seniority band · same employment typeSenior Product Manager, Workflows
Customer.io · LATAM, Canada, USA
same stated country · same seniority band · same employment typeAdjacent-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 Leadership. 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
Senior Staff Machine Learning Engineer - Content Platform
Spotify · USA
Same country · Similar work arrangement · Same employment type Advertised midpoint is about 63% higherAI Engineer, Marketing
Instructure · USA
Same country · Similar work arrangement · Same employment type Advertised midpoint is about 58% lowerThese 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?
AWS is one of the signals this listing shares with skills appearing often across current Data & AI roles opportunities.
Skills named in this listing
Shared with the current career sample
- AWS Seen in 38 sampled listings
- Google Cloud Seen in 24 sampled listings
- Machine Learning Seen in 47 sampled listings
- Python Seen in 84 sampled listings
Combinations also appearing together
- Python + SQL appeared together in 62 sampled listings
- Machine Learning + Python appeared together in 35 sampled listings
- AWS + Python appeared together in 32 sampled listings
Additional signals in this listing: Supply Chain
Application edge
- If these skills match your background, prepare one concrete example that shows how you used AWS, Google Cloud, and Machine Learning rather than relying on a keyword-only claim.
- The listing states about 8 years of required experience. Check that the dates and scope in your application make that experience easy to verify.
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.
At least about 8 years of experience required
The available wording contains an explicit minimum/required years-of-experience signal. Preferred years are tracked separately and never promoted into the required minimum.
AWS
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.
Google Cloud
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.
Machine Learning
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.
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.
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.
Supply Chain
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
The headline alone does not tell the whole story for Staff Data Scientist at hims & hers. The clearest decision context comes from the range is useful even though the comparison pool is small, remote flexibility can be weighed properly, and qualifications reveal more than the job title alone.
The range is useful even though the comparison pool is small
The USD range can be compared directly within its currency, although the present comparison sample is too shallow for a confident relative-pay ranking.
Remote flexibility can be weighed properly
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
At least about 8 years of experience required · 1 additional stated barrier. 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
1 recent enabled source can currently renew this vacancy across 1 source classes. That improves traceability, although it does not verify the employer itself.
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?
Only 8 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
The configured source supplied structured salary fields, a usable currency and a focused comparison value.
The source posting date is today.
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.
- The listing appears to require substantial prior experience.
- The description appears to mention an advanced degree requirement or preference.
- At least about 8 years of experience required
Still unknown
- There is not enough comparable salary evidence to establish this job’s current pay position.
- Visa sponsorship is not established by the available listing data.
Pay vs. barriers
Opportunity Efficiency not assessed
The current pay position is not reliable enough to weigh compensation against the stated barriers.
1 explicit required barrier is visible in the available wording.
The listing states at least about 8 years of required experience.
1 explicit Remote Freedom restriction signal is present and is shown separately from qualification requirements.
- Current comparable pay position is unavailable.
- Education requirement 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: At least about 8 years of experience required · 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.
The source provides structured salary information with a comparatively focused range, improving the usefulness of pay comparisons.
The source posting date is today.
Compensation intelligence
How competitive is this pay?
Only 8 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
Why this analysis?
- Only 8 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
- 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 hims & hers
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.
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
hims & hers — current opportunity snapshot
Employer Evidence Profile
How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.
Current sampled listings with advertised compensation evidence.
Current sampled listings whose work arrangement is clearer than unknown.
Current sampled listings with a supported country signal.
Current sampled listings with an explicit closing time.
Current sampled listings with a usable posting date.
Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.
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: Software engineering (9) · Finance Accounting (8) · Data & AI (5)
Visible seniority mix: Senior / lead (27) · Leadership (22)
Current employment-type mix: Full time (51) · Contract (2)
Most visible hiring countries in this sample: United States (49) · United Kingdom (3)
Other current roles from this employer
Sr. Application Security Engineer
USA
Senior / lead · Region-restricted remoteManager, Nonsterile Production Planning
USA
Senior / lead · Region-restricted remoteSr. Manager, Marketing Compliance
USA
Senior / lead · Region-restricted remoteCounsel, Regulatory (Pharmacy)
USA
Seniority not clearly specified · Region-restricted remoteThese are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.
Current disclosed salary medians by currency
- USD 192,500 median · 49 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
Remote with some uncertainty or restrictions
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.
The available listing details do not contain a clear enough sponsorship statement.
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 listing appears to require substantial prior experience.
- 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.
Sr. Director of Machine Learning
hims & hers · USA
Region-restricted remoteWhy comparable: Same seniority band · Same country · Similar work-location arrangement
Director of Product, Machine Learning & Decisioning Platforms
Upstart · Canada, USA
Region-restricted remoteWhy comparable: Same seniority band · Same country · Similar work-location arrangement
Principal Data Engineer
hims & hers · USA
Region-restricted remoteWhy 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.
Hims & Hers is the leading health and wellness platform, on a mission to help the world feel great through the power of better health. We are redefining healthcare by putting the customer first and delivering access to care that is affordable, accessible, and personal, from diagnosis to treatment to delivery. No two people are the same, so we provide access to personalized care designed for results. By normalizing health & wellness challenges and innovating on their solutions, we’re making better health outcomes easier to achieve.
Hims & Hers is a public company, traded on the NYSE under the ticker symbol “HIMS.” To learn more about the brand and offerings, you can visit hims.com/about and hims.com/how-it-works . For information on the company’s outstanding benefits, culture, and its talent-first flexible/remote work approach, see below and visit www.hims.com/careers-professionals.
About the Role:
As a Staff Data Scientist at Hims & Hers, you are a technical leader and a "force multiplier" for our data organization. You do not just solve the most difficult problems; you identify which problems are worth solving to move the needle for our customers. You will serve as a technical anchor, simplifying ambiguous problems into executable paths for the team.
In this role, you will bridge the gap between business strategy and production-ready machine learning. Whether you are building frameworks for growth, optimizing our supply chain, or refining marketing attribution, you will ensure our data products are technically sound, scalable, and built to deliver measurable business results.
You Will:
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Architect the 0-to-1 Foundation: Lead the design and implementation of automated ML systems. You know how to balance "doing it right" with "doing it fast," making pragmatic architectural choices (build vs. buy, simple vs. complex) while rolling up your sleeves to write production code and establish our core ML infrastructure.
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Translate Business Needs: Turn ambiguous business questions (from customer acquisition to churn dynamics) into concrete technical roadmaps that deliver clear, actionable results
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Drive Execution and Reliability: Lead the end-to-end deployment of ML products, ensuring they are not just accurate but robust, maintainable, and fully integrated into our production infrastructure
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Connect Technical & Business Goals: Partner across Engineering, Product, and Business to ensure our technical strategy is solving the right business problems and moving our core metrics
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Define Technical Standards: Act as a force multiplier by establishing the standards for model development. You will lead design docs and peer reviews that ensure our work is reproducible and integrates with the work of our Data and Analytics Engineering partners
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Own the Results: Take accountability for the full model lifecycle, from the initial data design through to the long-term performance and business value of production systems
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Mentorship & Coaching: Actively mentor Senior and Mid-level Data Scientists, elevating the technical bar and fostering a culture of continuous learning across the data organization.
Experience & Skills:
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8+ years of experience in Data Science or ML Engineering, with a proven track record of building production systems that deliver measurable business impact
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Technical Mastery: High proficiency in Python and SQL. Expert-level experience with the Python data stack (pandas, NumPy, scikit-learn) and at least one major ML framework (such as PyTorch or XGBoost/LightGBM)
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Systemic Problem Solving: Ability to work on unique issues requiring conceptual thinking and broad impact. You know how to build for long-term scalability while delivering immediate value
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Leadership & Influence: Proven ability to influence without authority. You can translate complex technical logic into compelling narratives for executive leadership
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Engineering Rigor: Experience with CI/CD, ML Ops, and managing the full lifecycle of models in a cloud-based production environment (AWS or GCP)
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Education: BS, MS, or PhD in a quantitative field (Data Science, Statistics, Economics, CS, Applied Math, etc.) or equivalent field expertise
Preferred Qualifications:
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0-to-1 Execution: Experience taking the very first machine learning models in an organization from exploratory notebooks to reliable, automated production pipelines.
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Advanced Business ML (Experience in 1-2 of the following):
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Customer Behavior & Propensity Modeling: Building predictive models for churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions.
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Applied Forecasting: Time-series forecasting, anomaly detection, or handling non-stationary data for demand or revenue planning.
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Optimization: Building engines for marketing spend, inventory management, or resource allocation.
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Causal Inference: Designing robust experiments (e.g., quasi-experiments, difference-in-differences) to measure true business impact beyond standard A/B testing.
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Our Benefits (there are more but here are some highlights):
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Competitive salary & equity compensation for full-time roles
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Unlimited PTO, company holidays, and quarterly mental health days
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Comprehensive health benefits including medical, dental & vision, and parental leave
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Employee Stock Purchase Program (ESPP)
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401k benefits with employer matching contribution
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Offsite team retreats
We are committed to building a workforce that reflects diverse perspectives and prioritizes ethics, wellness, and a strong sense of belonging. If you're excited about this role, we encourage you to apply—even if you're not sure if your background or experience is a perfect match.
Hims considers all qualified applicants for employment, including applicants with arrest or conviction records, in accordance with the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act, and any similar state or local fair chance laws.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Hims & Hers is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please contact us at accommodations@forhims.com and describe the needed accommodation. Your privacy is important to us, and any information you share will only be used for the legitimate purpose of considering your request for accommodation. Hims & Hers gives consideration to all qualified applicants without regard to any protected status, including disability. Please do not send resumes to this email address.
To learn more about how we collect, use, retain, and disclose Personal Information, please visit our Global Candidate Privacy Statement.
Listing provenance
Where did this job come from?
See where this vacancy came from and whether more than one current source can still support it. Source names help with traceability and attribution; they are not endorsements or guarantees.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
- Continuity
- One recent renewable source
- Source-class independence
- One renewable source class
- Last source observation
- Observed very recently
This records when High Paid Job last observed the vacancy in source evidence. It confirms source presence, not that the employer edited the vacancy at that time.
- 1 enabled source recently observed this vacancy.
Source confidence is about traceability and continuity. It is separate from application safety and should not be read as an employer reputation rating.
The vacancy is supplied by an external job publisher. Source information helps show where the listing came from; it is not a guarantee about the employer or vacancy.
Listing source: Jobicy. Destination: jobicy.com. The application opens in a new tab so you can keep this report open.