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. We are 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.

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?

Depending on your background, you may work across:

• 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

We care about selecting the right method, not using a particular framework.

Success is not a better offline metric.

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 simple:

the system should become better at operating the business because it has operated the business.

We care more about exceptional technical ability and judgment than matching a checklist. Strong candidates will have experience in several of:

• 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

Most ML systems learn from a dataset. Here, the decisions made by the model influence the data the model sees next. That creates a continuous loop: decision, intervention, outcome, learning, better decision.

The long-term opportunity is to build that capability once and apply it across a portfolio of businesses and increasingly broad commercial decisions.

• Real-world impact. The systems you build will run live commercial decisions across a portfolio of consumer brands, so you will see measurable economic outcomes from your work, not just offline benchmark improvements.
• Technical growth at the frontier. Causal decision systems that learn from their own interventions are still an open problem. You will work at the intersection of causal ML, bandit algorithms, and production engineering, with the latitude to choose the right method for the problem.
• Full ownership. You will own problems end to end, from framing and modeling through production deployment and evaluation.
• Competitive benefits. Comprehensive benefits including paid time off, 401(k) match, medical, dental, vision, supplemental coverage, and employee discounts across portfolio brands.