Senior Machine Learning Engineer, Causal & Decision Systems

CSC Generation • Austin, TX

6d

About The Position

CSC Generation is building closed-loop decision systems that use machine learning to operate consumer businesses more intelligently. We are starting with pricing and expanding into areas such as inventory, purchasing, promotions, marketing, and assortment. The Role You will help build systems that: estimate causal response + quantify uncertainty → choose actions → generate useful information → observe outcomes → update policies → evaluate challengers → deploy within guardrails We want to answer questions such as: - What happens because we change a price, rather than simply what happens next? - How should uncertainty affect a decision? - When should the system exploit what it knows versus experiment to learn? - Can we estimate the value of a challenger policy before fully deploying it? - How do we optimize economic outcomes while respecting inventory, margin, vendor, customer, and operational constraints?

Requirements

• Machine learning and statistical modeling experience.
• Causal inference and experimentation experience.
• Experience in recommendation, advertising, pricing, marketplace, credit, or other decision systems.
• Experience with bandits, reinforcement learning, optimization, or active learning.
• Uncertainty estimation experience.
• Counterfactual evaluation experience.
• Production ML systems experience.
• Proficiency in Python, SQL, and large behavioral datasets.

Nice To Haves

• 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.

Responsibilities

• Estimate causal response and quantify uncertainty.
• Choose actions based on learned policies.
• Generate useful information from system operations.
• Observe outcomes and update policies.
• Evaluate challenger policies.
• Deploy systems within guardrails.
• Optimize economic outcomes while respecting constraints (inventory, margin, vendor, customer, operational).

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed