Lead Data Scientist, Predictive Modeling & Causal Inference
About The Position
OneSix is seeking a Lead Data Scientist to join client teams as a senior technical partner. This role involves rigorous predictive modeling and production engineering, requiring expertise in deriving causal estimates, specifying generalized linear models, and debugging Spark jobs. The Lead Data Scientist will collaborate with client data science teams to enhance understanding and prediction of user behavior and business outcomes. Success requires strong judgment, the ability to build trust, and comfort with consulting work, including managing organically grown production systems, imperfect data, and business stakeholders with tight deadlines. The ideal candidate finds complexity energizing.
Requirements
- 7+ years of hands-on experience in predictive analytics, applied statistics, or machine learning, with a proven track record of deploying models into production.
- Deep fluency in predictive modeling techniques including generalized linear models, econometric methods, causal inference, and time-series forecasting (including deep learning-based approaches), with the ability to discuss trade-offs and failure modes based on experience.
- Strong software engineering fundamentals, including experience deploying and maintaining models in production, owning code quality, testing, and monitoring.
- Proficiency across the modern data stack (SQL, Spark, Python) and ability to work effectively in mature but occasionally messy production environments.
- Excellent communication and interpersonal skills, with the ability to build trust quickly, hold ground with evidence, and adapt.
- Keen client/stakeholder management capabilities.
- A graduate degree (M.S. or Ph.D.) in a quantitative or behavioral field (statistics, economics, computer science, cognitive science, or related) or equivalent demonstrated experience.
- Based in the US or Canada.
Nice To Haves
- Experience modeling user behavior related 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.
Responsibilities
- Design, build, and validate predictive models (GLMs, causal/econometric methods, deep learning-based forecasting) to analyze user behavior, retention, and business performance.
- Apply causal inference techniques (quasi-experimental design, uplift modeling, propensity methods, econometric tools) to guide client decisions.
- Manage the full model lifecycle: exploratory analysis, feature engineering, deployment, monitoring, and retraining in production.
- Write production-grade SQL, process data at scale in Spark, and build/deploy models in Python.
- Partner with client data science and analytics teams to translate business questions into modeling problems and communicate findings, even when unexpected.
- Communicate technical work clearly to both technical and non-technical stakeholders to build credibility and secure a seat at the strategic decision-making table.
- Apply engineering discipline to improve the reliability and maintainability of production environments.
Benefits
- Competitive compensation
- Company-paid medical, vision, dental, and wellness benefits for employees
- Company-provided home office equipment
- Flexible vacation and sick days
- Team-oriented and supportive working environment
- Company-sponsored events and swag
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What This Job Offers
Job Type
Full-time
Career Level
Senior
