Lead Data Scientist, Predictive Modeling & Causal Inference

Role Description

We're looking for a Lead Data Scientist to embed with key clients as a senior technical partner on their data science team. This is a player-coach role at the intersection of rigorous predictive modeling and production engineering.

You'll work closely with the client's data science team to shape how the organization understands and predicts user behavior and business outcomes. Success in this role depends as much on the strength of your judgment as your ability to earn trust in a room.

Comfort in consulting work is also a requirement, working with production systems that have grown organically over years, data that isn't always clean, and business stakeholders who need answers on a timeline.

What You'll Do

• Design, build, and validate predictive models, from GLMs and causal/econometric methods to deep learning-based forecasting.
• Apply causal inference techniques to move client stakeholders beyond correlation and toward actionable decisions.
• Own the full lifecycle of your models: from exploratory analysis and feature engineering through deployment, monitoring, and retraining.
• Work fluently across the stack, writing production-grade SQL, processing data at scale in Spark, and building and deploying models in Python.
• Partner directly with the client's data science and broader analytics team, translating ambiguous business questions into well-scoped modeling problems.
• Communicate technical work clearly to both technical and non-technical stakeholders.
• Bring engineering discipline to a production environment, improving reliability and maintainability incrementally.

Qualifications

• 7+ years of hands-on experience in predictive analytics, applied statistics, or machine learning.
• Deep fluency in predictive modeling techniques including generalized linear models, econometric methods, causal inference, and time-series forecasting.
• Strong software engineering fundamentals with experience deploying and maintaining models in production.
• 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 or equivalent demonstrated experience.
• Based in the US or Canada.

Nice to Have

• 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.

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.