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Causal Inference Machine Learning Postdoctoral Jobs in New York

Phia

Senior Machine Learning Engineer

New York, NY · On-site

$185K - $265K/yr

Overview As a Senior Machine Learning Engineer at Phia, you'll build and scale production ML ... Solid understanding of experiment design and causal inference, including A/B testing and offline ...

Walmart

(USA) Principal, Data Scientist

Hoboken, NJ

$132K - $264K/yr

This team leads advancements in generative AI, agentic intelligence, machine learning, measurement, and causal inference to redefine retail experiences, optimize operations, and develop new business ...

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Causal Inference Machine Learning Postdoctoral information

What is a Causal Inference Machine Learning Postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a Causal Inference Machine Learning Postdoctoral researcher, and why are they important?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by Causal Inference Machine Learning Postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in New York? For Causal Inference Machine Learning Postdoctoral jobs in New York, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in New York look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New York are:
What cities in New York are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in New York with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference

Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference

Headway

New York, NY • On-site

$249K - $312K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 15 days ago

Job description

1 in 4 people in the US have a treatable mental health condition, but most providers don't accept insurance, making therapy too expensive for most people. Headway's mission is to fix this by building a new mental healthcare system everyone can access. We started by solving the biggest barrier to care: insurance. The admin work - credentialing, claims, payment reconciliation - is a nightmare. We've automated that.
But we're going further. Over 75,000 providers across all 50 states run their practice on our software, serving over 1 million patients. We are building the best tools for therapists to run their entire practice, reimagining the experience of finding a therapist, and investing in the platform foundations to enable this at scale. We aren't just a billing layer; we are becoming the platform where care actually happens.
We're a Series D company with $325M+ in funding (a16z, Accel, Spark Capital, etc.), looking for exceptional people to help us achieve this mission. We want your time here to be the most meaningful experience of your career. Join us, and help change mental healthcare for the better.
Join us to build the truth engine behind better mental health outcomes.
As a Senior Staff Data Scientist, Bayesian Experimentation and Causal Inference, 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.
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. Think of it as a shared language that prevents the organization from treating a correlation like a law of physics, while still moving fast.
What you will do
  • 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, and represent Headway's measurement philosophy internally and externally when appropriate.
What will make you successful
  • 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. Compensation decisions are made holistically, ensuring fairness and alignment with market benchmarks while recognizing individual contributions and potential.
  • 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

#LI-MG1
We believe a team's strength is in its people, and we cannot achieve this mission without a team that reflects the diversity of this problem - across race, ethnicity, gender, sexuality, age, national origin, religion, family status, disability, military status, and experience. Headway is committed to the full inclusion of all qualified individuals. As part of this commitment, Headway will ensure that persons with disabilities are provided with reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or receive other benefits and privileges of employment, please inform the recruiter when they contact you to schedule your interview.
Headway participates in E-Verify. To learn more, click here.
A notice to Headway applicants: To protect yourself against phishing and recruitment fraud, please note that Headway only accepts applications through our official careers page at https://headway.co/careers. Headway will never refer you to external websites, ask for payment or personal information, or conduct interviews via messaging apps. All official communication will come from a @findheadway.com email address. If you are contacted by someone claiming to be from Headway via an unofficial channel, please do not share any information and report it as spam.

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