Senior Machine Learning Engineer, Causal & Decision Systems

CSC Generation is building closed-loop decision systems that use machine learning to operate consumer businesses more intelligently. You will help build systems that estimate causal response and quantify uncertainty, choose actions, generate useful information, observe outcomes, update policies, evaluate challengers, and deploy within guardrails.

Responsibilities:

• Work on causal and heterogeneous treatment-effect modeling, uncertainty estimation and calibration, contextual bandits, active learning, or sequential decision-making
• Develop policy learning and constrained optimization, counterfactual and off-policy evaluation, experimentation and champion/challenger systems, and production ML infrastructure
• Focus on selecting the right method, not using a particular framework

What success looks like:

• The systems you build should produce measurable economic lift in controlled experiments, generalize across businesses, learn from their own interventions, and safely automate an increasing share of real commercial decisions
• Over time, the goal is for the system to become better at operating the business because it has operated the business

Requirements:

• Experience in machine learning and statistical modeling, causal inference and experimentation, recommendation, advertising, pricing, marketplace, credit, or other decision systems
• Strong background in bandits, reinforcement learning, optimization, or active learning, uncertainty estimation, counterfactual evaluation, and production ML systems
• Proficiency in Python, SQL, and large behavioral datasets

Benefits:

• Competitive benefits package including primarily remote work, private medical and life insurance, additional paid time off, monthly allowances and reimbursements, employee discounts, and opportunities for professional growth