Senior Machine Learning Engineer, Causal & Decision Systems

CSC Generation Enterprise; Austin (TX); On-site; USD 140,000 - 180,000; Full time

Job summary

CSC Generation Enterprise seeks a talented machine learning engineer to help build closed-loop decision systems across pricing, inventory, and promotions. You’ll design causal models, quantify uncertainty, and develop policies that drive measurable economic lift in controlled experiments.

We value strong technical judgment over checklists, with experience in Python, SQL, and large behavioral datasets, plus interest in production ML systems and experimentation.

Qualifications

• Experience building causal models and counterfactual evaluation.
• Proficient in Python and SQL and working with large datasets.
• Experience with bandits, reinforcement learning, and constrained optimization.
• Ability to reason about uncertainty in decision making.

Responsibilities

• Develop causal and heterogeneous treatment-effect models.
• Estimate uncertainty and calibrate models for policy decisions.
• Experiment with champion/challenger systems and production ML pipelines.
• Collaborate across pricing, inventory, and marketing domains.
• Deploy and monitor models within guardrails.

Skills

Machine learning

Causal inference

Reinforcement learning

Python

SQL

Experimentation

Production systems

Job description

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?

What You’ll Work On

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.

What Success Looks Like

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.

What We’re Looking For

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.

Why This Role Is Different

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.