Causal Ml Data Jobs

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Today

Machine Learning Engineer, Causal Inference, Level 5

Snapchat Los Angeles, CA

Los Angeles, CA

Today

$178k - $313k/yr Full time Causal Inference Causal Machine Learning Causal Ml

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Job Description

Snap is hiring a Machine Learning Engineer focused on causal inference to design, build, and productionize causal machine learning models. This role also supports experimentation strategy and rigorous evaluation of technical tradeoffs across modeling, measurement, and decision-making.

Location and Work Style

Los Angeles, CA (onsite)
Snap uses a “default together” approach
Team members are expected to work in an office 4+ days per week

Salary and Compensation

Salary range: USD 178,000 - 313,000 per year
Zone A (CA, WA, NYC): $209,000 - $313,000 base
Zone B: $199,000 - $297,000 base
Zone C: $178,000 - $266,000 base
Eligible for equity in the form of RSUs
Starting pay may be negotiable within the salary range

Responsibilities

Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business
Develop and productionize causal machine learning solutions such as uplift modeling and heterogeneous treatment effect estimation using observational and experimental data
Design, analyze, and interpret A/B tests and quasi-experiments, partnering with product and engineering teams to shape experimentation strategies
Evaluate technical tradeoffs across model complexity, bias/variance, scalability, and interpretability
Perform code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
Contribute to rapid iteration cycles while ensuring methodological rigor

Requirements

Strong causal inference understanding, including modern approaches to estimating treatment effects (for example: meta learners, propensity score matching, instrumental variables)
Applied data science experience, including A/B testing, uplift modeling, and experimentation infrastructure
Proficiency in Python and common libraries such as pandas, NumPy, scikit-learn, and causal inference tooling
Ability to solve open-ended problems with a blend of statistical thinking and engineering pragmatism
Comfort working independently and collaborating with cross-functional teams
Strong communication and mentorship skills, including translating technical insights for non-technical partners
Bachelor’s degree in computer science, statistics, economics, or a related technical field (or equivalent practical experience)
Experience profile: 5+ years post-Bachelor’s in machine learning with hands-on causal inference or experimentation, or Master’s + 4+ years post-grad machine learning experience, or PhD + 2 years post-grad machine learning experience
Demonstrated experience building causal models to support product decision-making and policy evaluation
Experience designing and analyzing online experiments and leveraging causal ML in production systems

Technologies

Python
pandas
NumPy
scikit-learn
CausalM
CausalML
EconML
DoWhy

Benefits

Paid parental leave
Comprehensive medical coverage
Emotional and mental health support programs
Compensation packages intended to align with Snap’s long-term success

Preferred Qualifications

Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research
Experience with causal inference libraries including CausalML, EconML, or DoWhy
Background deploying models in production and working with ML or experimentation infrastructure
Deep understanding of experimentation nuances, including intent-to-treat (ITT) versus ghost ad methodologies, and trade-offs between frequentist and Bayesian inference for decision-making under uncertainty
Experience applying causal inference in domains such as personalization and ad or marketplace dynamics

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13d ago

Senior Data Scientist

Synapse Health Remote 🌍 Remote

Remote

13d ago

Full time Senior Agentic Ai Causal Ml

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Job Description

Synapse Health is seeking a Senior Data Scientist to take ownership of end-to-end modeling work across vendor matching, order routing, and supply chain optimization in DME operations. This role partners closely with data engineering to move models from experimentation to production and supports expansion into Revenue Cycle Management and Finance initiatives.

Key Responsibilities

Own models end to end across vendor matching, order routing, or supply chain optimization, expanding into new problem areas as priorities shift
Build components of confidence thresholds and decision logic that move the team’s efforts toward agentic AI
Quantify the impact of your work using measurable results rather than assumptions
Ship models end to end, from experimentation through production, in close partnership with data engineering
Select appropriate technical approaches for well-scoped problems, including predictive ML, causal ML, reinforcement learning, or operations research, with support from senior team members on complex judgment calls
Write clear technical documentation for your own work
Participate in quarterly planning and take on leverage-sequenced pieces of the roadmap
Remain flexible as scope expands into Revenue Cycle Management, Finance, and other domains
Operate effectively in a fast-moving, sometimes ambiguous environment

Required Qualifications

Master’s degree required in a quantitative field (Computer Science, Statistics, Data Science, Operations Research, or related)
4–7 years of data science experience
Prior experience at an early-stage healthcare startup, including hands-on work with claims data and other healthcare data
Strong technical foundation in standard predictive ML (classification, regression, forecasting)
Hands-on proficiency in Python and SQL, including the ability to write, debug, and optimize production-quality code beyond notebook prototypes
Comfort with the full software development lifecycle and fluency with GitHub workflows, including version control, branching strategies, pull requests, and code review
Track record shipping ML models into production in partnership with data engineering
Ability to produce clear technical documentation
Effective verbal and written communication, including presenting findings to technical and non-technical stakeholders
Strong analytical and organizational skills, including managing multiple workstreams and priorities
Comfort working in high-pressure settings where priorities shift and requirements may not be fully defined

Technologies

Python
SQL
GitHub

Problems You Will Contribute To

Vendor matching: build and iterate on models that determine which vendor fulfills each incoming order
Order routing: develop predictive models that flag orders at risk of delay based on historical patterns
Supply chain optimization: apply frameworks and methods to identify and quantify specific bottlenecks
Agentic AI: implement and test components of decision logic aligned with architecture led by more senior team members

Additional Strengths That May Stand Out

Reinforcement learning for sequencing decisions over time
Operations research methods including queueing theory, network flow optimization, and discrete event simulation
Causal inference methods such as difference-in-differences, regression discontinuity, or heterogeneous treatment effects
Health economics or healthcare claims data experience
Exposure to agentic AI tools or frameworks

Benefits

Professional growth opportunities with compelling career paths
Healthy work-life balance supported by flexible paid time off (PTO)
Comprehensive benefits package, including medical, dental, vision, STD & LTD insurance for full-time team members
401(k) savings plan with employer matching contributions

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