Senior Machine Learning Engineer, Causal & Decision Systems

estimate causal response + quantify uncertainty → choose actions → generate useful information → observe outcomes → update policies → evaluate challengers → deploy within guardrails

• 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.

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;

• 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.

Here, the decisions made by the model influence the data the model sees next.

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. Originally posted on Himalayas