Job Description:
• Design, build, and validate predictive models, including GLMs, causal/econometric methods, and deep learning-based forecasting
• Apply causal inference techniques such as quasi-experimental design, uplift modeling, propensity methods, and related econometric tools
• Own the full model lifecycle from exploratory analysis and feature engineering through deployment, monitoring, and retraining in production
• Write production-grade SQL, process data at scale in Spark, and build and deploy models in Python
• Partner with client data science and analytics teams to translate ambiguous business questions into scoped modeling problems
• Communicate technical work to technical and non-technical stakeholders and inform strategic decisions
• Improve reliability and maintainability in mature production environments
Requirements:
• 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 generalized linear models, econometric methods, causal inference, and time-series forecasting, including deep learning-based forecasting approaches
• Strong software engineering fundamentals, including production model deployment and maintenance, code quality, testing, and monitoring
• Proficiency in SQL, Spark, and Python
• Excellent communication and interpersonal skills; strong client/stakeholder capability
• 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
• Based in the US or Canada
Benefits:
• 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