{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/uplift-modeling"},"x-facet":{"type":"skill","slug":"uplift-modeling","display":"Uplift Modeling","count":3},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8514594a-2d3"},"title":"Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference","description":"<p>We are looking for a Senior Staff Data Scientist to join our team. As a Senior Staff Data Scientist, you will be the company-wide owner of how Headway learns from data, especially when the stakes are high and the signal is noisy. You will report directly to the Head of Data and serve as a core leader for standards, frameworks, and decision quality across Product, Growth, Ops, and Finance.</p>\n<p>Your work will set the default methods for how we answer questions like:</p>\n<ul>\n<li>Did this actually cause the outcome we care about?</li>\n<li>How sure are we, and what should we do given that uncertainty?</li>\n<li>What evidence is strong enough to change strategy, policies, or spend?</li>\n</ul>\n<p>A major objective of this role is to build and institutionalize a clear map of “what we know” about patients, providers, and payers, with explicit confidence levels that tie directly to business action.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own causal inference and experimentation standards across Headway.</li>\n<li>Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including when and how to use Bayesian methods.</li>\n<li>Build the confidence ladder for company knowledge.</li>\n<li>Create a clear, shared framework that maps findings to levels of confidence (for example 1–10), where lower levels reflect correlation and early directional evidence, mid levels reflect increasingly credible causal inference, and the highest levels reflect stable, repeatable, decision-grade truths.</li>\n<li>Operationalize it so it shows up in artifacts teams actually use: PRDs, launch reviews, growth planning, quarterly business reviews, and postmortems.</li>\n<li>Design the learning strategy for our hardest questions.</li>\n<li>Lead the approach for ambiguous, high impact domains like provider activation and retention, payer economics and policies, patient conversion and engagement, and marketplace dynamics.</li>\n<li>Recommend the right combination of randomized experiments, stepped rollouts, geo tests, natural experiments, and observational designs.</li>\n<li>Raise the organization’s statistical maturity.</li>\n<li>Introduce and standardize Bayesian experimentation practices where it improves speed and decision quality (priors, posterior interpretation, sequential decision rules, credible intervals, expected value framing).</li>\n<li>Build training, playbooks, and reusable tooling.</li>\n<li>Be the escalation point for difficult measurement problems.</li>\n<li>Tackle issues like interference and spillovers, network effects, selection bias, noncompliance, measurement error, multiple comparisons, seasonality, and Simpson’s paradox showing up in real life and causing confusion.</li>\n<li>Partner with Data Platform and Engineering to make rigor scalable.</li>\n<li>Ensure experimentation and inference are supported by instrumentation, logging, metric definitions, semantic layers, and monitoring.</li>\n<li>Help define the minimal foundations required for trustworthy learning.</li>\n<li>Build a culture of clear claims.</li>\n<li>Establish norms for separating facts, estimates, assumptions, and uncertainties.</li>\n<li>Make it easy for teams to say “we do not know yet” without losing momentum, and easy for leaders to understand what is safe to act on.</li>\n<li>Mentor and set the bar.</li>\n<li>Coach other data scientists and analytics leaders.</li>\n<li>Create review standards for causal work,</li>\n<li>Support hiring for methodological depth,</li>\n<li>Represent Headway’s measurement philosophy internally and externally when appropriate.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>12+ years of experience applying causal inference, experimentation, and advanced statistics to real-world product, growth, or operational decisions (or equivalent depth demonstrated through scope and outcomes).</li>\n<li>Deep expertise in causal inference across randomized and observational settings, including practical strategy for when clean experiments are not possible.</li>\n<li>Deep expertise in Bayesian methods for experimentation and decision-making, and strong judgment about when Bayesian approaches outperform frequentist defaults and when they do not.</li>\n<li>Strong SQL and strong proficiency in Python or R, including building reusable analysis tools and improving team workflows.</li>\n<li>Track record of setting org-wide standards that materially improved decision quality and execution velocity.</li>\n<li>Executive-level communication and influence: you can drive alignment across Product, Growth, Ops, Finance, and Engineering.</li>\n<li>Comfort operating in ambiguity, and the ability to turn it into crisp frameworks, clear recommendations, and measurable outcomes.</li>\n<li>Motivation for our mission: improving access and affordability in mental healthcare.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience in marketplaces, healthcare, insurance, or other regulated and complex incentive systems.</li>\n<li>Experience with experimentation under interference and network effects.</li>\n<li>Experience building experimentation platforms, analysis libraries, or statistical tooling used broadly across an organization.</li>\n<li>Familiarity with causal graphs, uplift modeling, and decision theory framing (expected value, value of information).</li>\n</ul>\n<p>Compensation and Benefits: The expected base pay range for this position is $249,600 - $312,000, based on a variety of factors including qualifications, experience, and geographic location. In addition to base salary, this role may be eligible for an equity grant, depending on the position and level. We are committed to offering a comprehensive and competitive total rewards package, including robust health and wellness benefits, retirement savings, and meaningful ownership opportunities through equity.</p>\n<ul>\n<li>Benefits offered include:</li>\n<li>Equity compensation</li>\n<li>Medical, Dental, and Vision coverage</li>\n<li>HSA / FSA</li>\n<li>401K</li>\n<li>Work-from-Home Stipend</li>\n<li>Therapy Reimbursement</li>\n<li>16-week parental leave for eligible employees</li>\n<li>Carrot Fertility annual reimbursement and membership</li>\n<li>13 paid holidays each year as well as a Holiday Break during the week between December 25th and December 31st</li>\n<li>Flexible PTO</li>\n<li>Employee Assistance Program (EAP)</li>\n<li>Training and professional development</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_8514594a-2d3","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Headway","sameAs":"https://www.headway.com/","logo":"https://logos.yubhub.co/headway.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/headway/jobs/5751656004","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$249,600 - $312,000","x-skills-required":["causal inference","experimentation","advanced statistics","Bayesian methods","SQL","Python","R","data analysis","data science"],"x-skills-preferred":["marketplaces","healthcare","insurance","complex incentive systems","experimentation under interference and network effects","experimentation platforms","analysis libraries","statistical tooling","causal graphs","uplift modeling","decision theory framing"],"datePosted":"2026-04-18T15:57:22.943Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Healthcare","skills":"causal inference, experimentation, advanced statistics, Bayesian methods, SQL, Python, R, data analysis, data science, marketplaces, healthcare, insurance, complex incentive systems, experimentation under interference and network effects, experimentation platforms, analysis libraries, statistical tooling, causal graphs, uplift modeling, decision theory framing","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":249600,"maxValue":312000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_27ecbf0a-523"},"title":"Senior GTM Data Scientist","description":"<p>We are building a GTM Data Products team to embed machine learning and AI directly into our Sales and Marketing workflows. We are hiring a Senior GTM Data Scientist to design and deploy predictive systems that materially improve customer acquisition, sales efficiency, and customer retention and expansion.</p>\n<p>This is not a reporting role. This role owns end-to-end data products - from problem framing and modeling to deployment and operational integration - that directly influence how our GTM organization prioritizes leads, manages accounts, allocates resources, and drives revenue.</p>\n<p>You’ll work closely with Marketing, Sales, and RevOps leadership to build ML-powered systems that change how decisions are made at scale.</p>\n<p><strong>What will I be doing?</strong></p>\n<ul>\n<li>Build Revenue-Impacting ML Systems</li>\n<li>Develop, deploy, optimize predictive models (lead scoring, account prioritization, marketing attribution, revenue estimation)</li>\n<li>Productionize models into operational systems (Salesforce, Marketo, outbound workflows)</li>\n<li>Monitor model performance and iterate for measurable business lift</li>\n<li>Design and implement experimentation frameworks (A/B testing, holdouts, incremental lift measurement)</li>\n<li>Apply advanced techniques when appropriate (e.g., causal inference, uplift modeling, segmentation, LTV modeling)</li>\n</ul>\n<p>You don’t just build models - you ensure they change behavior.</p>\n<p><strong>Own End-to-End Data Products</strong></p>\n<ul>\n<li>Translate ambiguous business problems into clear, measurable objectives</li>\n<li>Define GTM data products vision, success metrics, and roadmap</li>\n<li>Ensure integration into existing workflows and systems</li>\n<li>Lead stakeholder alignment and change management</li>\n<li>Secure buy-in from system owners before replacing or enhancing existing solutions</li>\n</ul>\n<p>You operate as a mini GM for your data products.</p>\n<p><strong>Architect Scalable Data Foundations</strong></p>\n<ul>\n<li>Design robust data pipelines and modeling infrastructure in collaboration with Data Engineering / Data Infrastructure</li>\n<li>Ensure data quality, governance, and reproducibility</li>\n<li>Elevate the team’s standards for experimentation, documentation, and knowledge sharing</li>\n<li>Push adoption of new tools and AI capabilities where appropriate</li>\n</ul>\n<p>You raise the technical bar for the GTM organization.</p>\n<p><strong>What impact might I have?</strong></p>\n<p>Within 6-12 months, you might:</p>\n<ul>\n<li>Launch predictive models that materially improve conversion, expansion, or retention</li>\n<li>Reduce inefficiencies in Sales workflows through automation</li>\n<li>Help leadership make investment decisions backed by rigorous data science</li>\n<li>Influence GTM strategy through quantitative insight and modeling</li>\n</ul>\n<p>Success is measured in business outcomes - not dashboards built.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_27ecbf0a-523","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Intercom","sameAs":"https://www.intercom.com/","logo":"https://logos.yubhub.co/intercom.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/intercom/jobs/7652268","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$197,600 - $246,713","x-skills-required":["Expert-level SQL","Advanced Python or R for modeling and experimentation","Strong foundation in statistics and experimental design","Predictive modeling","Feature engineering"],"x-skills-preferred":["Causal inference or uplift modeling","Model deployment & monitoring"],"datePosted":"2026-04-18T15:55:52.628Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Expert-level SQL, Advanced Python or R for modeling and experimentation, Strong foundation in statistics and experimental design, Predictive modeling, Feature engineering, Causal inference or uplift modeling, Model deployment & monitoring","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":197600,"maxValue":246713,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4d4df1fe-7ee"},"title":"Data Scientist, Financial Engineering","description":"<p><strong>Data Scientist, Financial Engineering</strong></p>\n<p><strong>About the Team</strong></p>\n<p>OpenAI’s <strong>Financial Engineering (FinEng)</strong> team powers how revenue flows through our products—pricing &amp; packaging, checkout, payments, subscriptions, and the financial infrastructure behind them. We partner with Product, Engineering, Risk, Finance, and Go-to-Market to make paying for OpenAI products seamless, reliable, and efficient worldwide.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Data Scientist on FinEng, you’ll own the analytics and experimentation that improve our <strong>checkout and payments</strong>, <strong>subscriptions</strong>, and <strong>pricing &amp; monetization</strong> systems. You’ll define the metrics that matter, build the source-of-truth data assets, and design experiments that increase conversion, reduce churn and payment failures, and expand global payment method coverage. Your work will directly influence revenue, customer experience, and how we scale internationally.</p>\n<p>This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>In this role, you will</strong></p>\n<ul>\n<li>Own checkout &amp; payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency.</li>\n</ul>\n<ul>\n<li>Build and run the experimentation program for in-house checkout—define success metrics and guardrails, execute staged rollouts, and use offline incrementality when online tests aren’t feasible.</li>\n</ul>\n<ul>\n<li>Create operational visibility and source-of-truth data with FinEng Data Engineering—land team-level metrics, SLAs, and self-serve dashboards that drive proactive action.</li>\n</ul>\n<ul>\n<li>Lead subscription, retention, and monetization analytics—ship launch-readiness for new subscription features, reduce involuntary churn (e.g., targeted retrials/nudges), and develop elasticity/FX frameworks toward pricing optimality.</li>\n</ul>\n<p><strong>You might thrive in this role if you have</strong></p>\n<ul>\n<li>5+ years in a quantitative role (data science, product analytics, or experimentation) in high-growth or fintech environments</li>\n</ul>\n<ul>\n<li>Fluency in <strong>SQL</strong> and <strong>Python</strong>, with a track record designing and interpreting A/B tests and quasi-experiments</li>\n</ul>\n<ul>\n<li>Experience building product metrics from scratch and operationalizing them for decision-making</li>\n</ul>\n<ul>\n<li>Excellent communication skills with PMs, engineers, risk/finance partners, and executives</li>\n</ul>\n<ul>\n<li>Strategic instincts beyond significance tests—clear thinking about tradeoffs (conversion vs. risk vs. cost vs. user experience)</li>\n</ul>\n<p><strong>You could be an especially great fit if you have</strong></p>\n<ul>\n<li>Payments, checkout, or subscription analytics experience (PSPs, bank rails, disputes/refunds, risk, e-commerce)</li>\n</ul>\n<ul>\n<li>Background in <strong>offline incrementality</strong> methods, uplift modeling, CUPED/causal inference, or counterfactual evaluation</li>\n</ul>\n<ul>\n<li>Experience with internationalization/local payments, FX, and pricing &amp; packaging strategy</li>\n</ul>\n<ul>\n<li>Comfort building operational analytics (alerting, SLIs/SLOs) and partnering closely with data engineering</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick and safe time (1 hour per 30 hours worked)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p><strong>Salary</strong></p>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus for eligible employees and benefits.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4d4df1fe-7ee","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenAI","sameAs":"https://jobs.ashbyhq.com","logo":"https://logos.yubhub.co/openai.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openai/898a87fb-4cb8-450e-9840-ee5dc710a57d","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$230K – $385K","x-skills-required":["SQL","Python","A/B testing","quasi-experiments","product metrics","operational analytics"],"x-skills-preferred":["payments","checkout","subscription analytics","offline incrementality","uplift modeling","CUPED/causal inference","counterfactual evaluation","internationalization/local payments","FX","pricing & packaging strategy"],"datePosted":"2026-03-06T18:32:44.006Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"SQL, Python, A/B testing, quasi-experiments, product metrics, operational analytics, payments, checkout, subscription analytics, offline incrementality, uplift modeling, CUPED/causal inference, counterfactual evaluation, internationalization/local payments, FX, pricing & packaging strategy","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":385000,"unitText":"YEAR"}}}]}