Description
CSC Generation is building closed-loop decision systems that use machine learning to operate consumer businesses more intelligently. The company is starting with pricing and expanding into areas such as inventory, purchasing, promotions, marketing, and assortment.
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.
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.
Responsibilities:
- Causal and heterogeneous treatment-effect modeling
- Uncertainty estimation and calibration
- Contextual bandits, active learning, or sequential decision-making
- Policy learning and constrained optimization
- Counterfactual and off-policy evaluation
- Experimentation and champion/challenger systems
- Production ML infrastructure, monitoring, and automated deployment
Requirements:
- Machine learning and statistical modeling
- Causal inference and experimentation
- Recommendation, advertising, pricing, marketplace, credit, or other decision systems
- Bandits, reinforcement learning, optimization, or active learning
- Uncertainty estimation
- Counterfactual evaluation
- Production ML systems
- Python, SQL, and large behavioral datasets
Benefits:
- Comprehensive benefits including paid time off, 401(k) match, medical, dental, vision, supplemental coverage, and employee discounts across portfolio brands.