Senior Machine Learning Engineer, Causal & Decision Systems

Cscgeneration 2; Austin (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

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 inventory, purchasing, promotions, marketing, and assortment.

As a Senior ML Engineer you will architect models that estimate causal effect, quantify uncertainty, and guide actions while observing outcomes and updating policies. You will deploy within guardrails and scale across a portfolio of businesses.

Qualifications

• Experience in machine learning and statistical modeling.
• Experience in causal inference and experimentation.
• Experience with decision systems in pricing, marketplace, or related domains.
• Knowledge of bandits, reinforcement learning, optimization, or active learning.
• Experience with uncertainty estimation and counterfactual evaluation.
• Proficiency in Python, SQL, and handling large behavioral datasets.

Responsibilities

• Build causal and heterogeneous treatment-effect models.
• Develop uncertainty estimation and calibration methods.
• Develop contextual bandits, active learning, or sequential decision-making.
• Work on policy learning and constrained optimization.
• Perform counterfactual and off-policy evaluation.
• Support experimentation and champion/challenger systems.
• Contribute to production ML infrastructure, monitoring, and automated deployment.

Skills

Machine learning

Statistical modeling

Causal inference

Experimentation

Uncertainty estimation

Python

SQL

Large datasets

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.