Lead Data Scientist, Predictive Modeling & Causal Inference
$200,000–$230,000 year
Remote
Job Summary
Design, build, and validate predictive models ranging from generalized linear models to deep learning forecasting to answer questions about user behavior and business performance. Apply causal inference techniques such as uplift modeling and quasi-experimental design to move stakeholders beyond correlation toward actionable decisions. Own the full model lifecycle, moving from exploratory analysis and feature engineering through deployment and monitoring in live production environments. Work fluently across the stack using SQL, Spark, and Python to deliver shipped solutions without hand-offs. Partner directly with client data science teams to translate ambiguous business questions into scoped modeling problems while communicating technical work to both technical and non-technical audiences. Bring engineering discipline to mature but imperfect production systems, improving reliability and maintainability incrementally. Embed as a senior technical partner with key clients to shape how organizations understand and predict outcomes.
Required Qualifications
- 7+ years of hands-on experience in predictive analytics, applied statistics, or machine learning, with a track record of taking models from concept into production
- Deep fluency in predictive modeling techniques spanning generalized linear models, econometric methods, causal inference, and time-series forecasting
- Strong software engineering fundamentals: you've deployed and maintained models in production, not just prototyped them in a notebook, and you're comfortable owning code quality, testing, and monitoring for the solutions you build
- Proficiency across the modern data stack (e.g., SQL, Spark, and Python)
- Excellent communication and interpersonal skills
- A graduate degree (M.S. or Ph.D.) in a quantitative or behavioral field ( statistics, economics, computer science, cognitive science, or a related discipline) or equivalent demonstrated experience
- Comfort in consulting work
- Based in the US or Canada
Desired Qualifications
- Experience modeling user behavior as it relates to downstream outcomes like churn, lifetime value, engagement, or propensity to convert
- A Ph.D. in cognitive science, behavioral economics, or a similarly human-behavior-oriented quantitative field
- Prior consulting or professional services experience, particularly in client-facing technical roles
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