Your responsibilities will include identifying areas where ML can create value for our customers, identifying the right ML framing of product problems, working with teammates and Product and Design stakeholders, conducting exploratory data analysis and research, deeply understanding the problem area, researching and identifying the right algorithms and tools, being pragmatic, but innovating right to the cutting-edge when needed, performing offline evaluation to gather evidence an algorithm will work, working with engineers to bring prototypes to production, planning, measuring & socializing learnings to inform iteration, and partnering deeply with the rest of team, and others, to build excellent ML products.

To be successful in this role, you will need to have broad applied machine learning knowledge, 3-5 years applied ML experience, practical stats knowledge (experiment design, dealing with confounding etc), intermediate programming skills, strong communication skills, both within engineering teams and across disciplines, comfort with ambiguity, typically have advanced education in ML or related field (e.g. MSc), and scientific thinking skills. Bonus skills and attributes include track record shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area. e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering, strong stats or math background, visualization, data skills, SQL, matplotlib, etc.

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Your work will set the default methods for how we answer questions like:

• Did this actually cause the outcome we care about?
• How sure are we, and what should we do given that uncertainty?
• What evidence is strong enough to change strategy, policies, or spend?

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.

Responsibilities:

• Own causal inference and experimentation standards across Headway.
• Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including when and how to use Bayesian methods.
• Build the confidence ladder for company knowledge.
• 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.
• Operationalize it so it shows up in artifacts teams actually use: PRDs, launch reviews, growth planning, quarterly business reviews, and postmortems.
• Design the learning strategy for our hardest questions.
• Lead the approach for ambiguous, high impact domains like provider activation and retention, payer economics and policies, patient conversion and engagement, and marketplace dynamics.
• Recommend the right combination of randomized experiments, stepped rollouts, geo tests, natural experiments, and observational designs.
• Raise the organization’s statistical maturity.
• Introduce and standardize Bayesian experimentation practices where it improves speed and decision quality (priors, posterior interpretation, sequential decision rules, credible intervals, expected value framing).
• Build training, playbooks, and reusable tooling.
• Be the escalation point for difficult measurement problems.
• 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.
• Partner with Data Platform and Engineering to make rigor scalable.
• Ensure experimentation and inference are supported by instrumentation, logging, metric definitions, semantic layers, and monitoring.
• Help define the minimal foundations required for trustworthy learning.
• Build a culture of clear claims.
• Establish norms for separating facts, estimates, assumptions, and uncertainties.
• 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.
• Mentor and set the bar.
• Coach other data scientists and analytics leaders.
• Create review standards for causal work,
• Support hiring for methodological depth,
• Represent Headway’s measurement philosophy internally and externally when appropriate.

Requirements:

• 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).
• Deep expertise in causal inference across randomized and observational settings, including practical strategy for when clean experiments are not possible.
• 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.
• Strong SQL and strong proficiency in Python or R, including building reusable analysis tools and improving team workflows.
• Track record of setting org-wide standards that materially improved decision quality and execution velocity.
• Executive-level communication and influence: you can drive alignment across Product, Growth, Ops, Finance, and Engineering.
• Comfort operating in ambiguity, and the ability to turn it into crisp frameworks, clear recommendations, and measurable outcomes.
• Motivation for our mission: improving access and affordability in mental healthcare.

Nice to have:

• Experience in marketplaces, healthcare, insurance, or other regulated and complex incentive systems.
• Experience with experimentation under interference and network effects.
• Experience building experimentation platforms, analysis libraries, or statistical tooling used broadly across an organization.
• Familiarity with causal graphs, uplift modeling, and decision theory framing (expected value, value of information).

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.

• Benefits offered include:
• Equity compensation
• Medical, Dental, and Vision coverage
• HSA / FSA
• 401K
• Work-from-Home Stipend
• Therapy Reimbursement
• 16-week parental leave for eligible employees
• Carrot Fertility annual reimbursement and membership
• 13 paid holidays each year as well as a Holiday Break during the week between December 25th and December 31st
• Flexible PTO
• Employee Assistance Program (EAP)
• Training and professional development

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You'll collaborate with our technical team to train models on human interactions, problem-solving, and discussions; refine annotation tools; and select/create challenging problems in statistics to boost performance.

All of our AI Tutor roles potentially involve gathering or providing data in text, voice, and video formats, including annotations, audio recordings, or video sessions,tasks with which candidates must be comfortable.

Responsibilities:

• Use proprietary software applications to provide input/labels on defined projects.
• Support and ensure the delivery of high-quality curated data.
• Play a pivotal role in supporting and contributing to the training of new tasks, working closely with the technical staff to ensure the successful development and implementation of cutting-edge initiatives/technologies.
• Interact with the technical staff to help improve the design of efficient annotation tools.
• Choose problems from statistical domains that align with your expertise, focusing on areas such as probability theory, inferential statistics, regression modelling, multivariate analysis, stochastic processes, Bayesian methods, experimental design, and data-driven applications where you can confidently provide detailed solutions and evaluate model responses.

Basic Qualifications:

• Must have either (a) a Master’s or PhD in Statistics (or Mathematics with a specialisation in statistics or probability) or (b) a medal in an International Math Olympiad (IMO), International Statistical Olympiad, or similar competition.
• Proficiency in reading and writing, both in informal and professional English.
• Strong ability to navigate various information resources and databases.
• Outstanding communication, interpersonal, analytical, and organisational capabilities.
• Solid reading comprehension skills combined with capacity to exercise autonomous judgement even when presented with limited data/material.
• A strong passion for and commitment to technological advancements and innovation.

Preferred Skills and Experience:

• PhD in Statistics or a closely related quantitative field.
• Peer-reviewed publications in statistics, probability, or applied statistical modelling.
• Previous AI Tutoring experience and/or experience teaching college-level statistics courses.

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit.
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required. On average most projects may involve at least 10 hours per week to achieve deliverables effectively though this is not a fixed commitment and depends on the scope of work. Contractors have full flexibility to set their own hours and determine the exact amount of time needed to complete deliverables.
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs.
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time.
• We are unable to provide visa sponsorship.
• For those who will be working from a personal device, your computer must be a Chromebook, Mac with MacOS 11.0 or later, or Windows 10 or later.

Compensation and Benefits:

US based candidates: $45/hour - $75/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates: Information will be provided to you during the recruitment process.

Benefits vary based on employment type, location and jurisdiction. Benefits for eligible U.S. based positions include health insurance, 401(k) plan, and paid sick leave. Specific details and role specific information will be provided to you during the interview process.

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You'll collaborate with our technical team to train models on human interactions, problem-solving, and discussions; refine annotation tools; and select/create challenging problems in statistics to boost performance.

Responsibilities:

• Use proprietary software applications to provide input/labels on defined projects.
• Support and ensure the delivery of high-quality curated data.
• Play a pivotal role in supporting and contributing to the training of new tasks, working closely with the technical staff to ensure the successful development and implementation of cutting-edge initiatives/technologies.
• Interact with the technical staff to help improve the design of efficient annotation tools.
• Choose problems from statistical domains that align with your expertise, focusing on areas such as probability theory, inferential statistics, regression modeling, multivariate analysis, stochastic processes, Bayesian methods, experimental design, and data-driven applications where you can confidently provide detailed solutions and evaluate model responses.

Basic Qualifications:

• Must have either (a) a Master’s or PhD in Statistics (or Mathematics with a specialization in statistics or probability) or (b) a medal in an International Math Olympiad (IMO), International Statistical Olympiad, or similar competition.
• Proficiency in reading and writing, both in informal and professional English.
• Strong ability to navigate various information resources and databases.
• Outstanding communication, interpersonal, analytical, and organizational capabilities.
• Solid reading comprehension skills combined with capacity to exercise autonomous judgment even when presented with limited data/material.
• A strong passion for and commitment to technological advancements and innovation.

Preferred Skills and Experience:

• PhD in Statistics or a closely related quantitative field.
• Peer-reviewed publications in statistics, probability, or applied statistical modeling.
• Previous AI Tutoring experience and/or experience teaching college-level statistics courses.

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We are seeking an associate-level quantitative researcher to support and expand our growing suite of models for estimating the value of private assets and producing next-generation, real-time private market indices.

Role Description

The role centers on the development, operation, and continuous enhancement of Nowcasting models—managing day-to-day index estimation, improving model performance, and ensuring a robust, high-quality production process. A key challenge will be adapting these models to new situations, data conditions, and asset-class features, and determining when extensions or methodological changes are appropriate.

Key Responsibilities

• Manage and enhance the team’s existing Nowcasting model for private assets, including calibration, monitoring, diagnostics, and performance evaluation.
• Conduct empirical research to calibrate models to private market data and assess model performance through backtesting, benchmarking, and robustness analysis.
• Implement, maintain, and productionize model codebases, ensuring reliability, transparency, and reproducibility.
• Partner with internal stakeholders to understand use cases, communicate model behavior and limitations, and support applications of the models in investment and analytical workflows.

Qualifications

• Master’s degree in Financial Engineering, Finance, Statistics/Econometrics, Mathematics, Computer Science, or a related quantitative field; PhD preferred.
• 3+ years of experience in financial modeling role or equivalent.
• Strong grounding in empirical modeling techniques, especially time-series analysis and Bayesian methods.
• Demonstrated ability to conduct rigorous empirical research, including handling complex datasets and designing well-structured analyses.

Our benefits

To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.

Our hybrid work model

BlackRock’s hybrid work model is designed to enable a culture where collaboration and apprenticeship enrich the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities.

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