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Be ready to discuss causal identification vs correlation, A/B and quasi-experiments, off-policy evaluation, and how you’d productionize decision models for retail-scale traffic. Use public CSC Generation deal context (e.g. Backcountry) only as business framing—not as insider claims.
Strong fit if you ship senior-level ML for decisions under uncertainty and like multi-brand commerce platforms. Thin public employee-sentiment and pay data; treat culture and comp as open interview topics.
As Senior Machine Learning Engineer, Causal & Decision Systems at CSC Generation, expect work on models and experiments that drive product, pricing, merchandising, or ops decisions across a multi-brand retail platform—framing causal questions, building estimators or decision policies, validating offline, and partnering with data, product, and brand teams so recommendations ship safely at scale.
No occupation or cert package was supplied. Role title implies strength in causal inference, experimentation, decision systems, and production ML—not formal cert paths.
The listing marks remote support with Austin, TX as the location base—confirm hybrid vs fully remote in the process.
Public coverage describes a tech platform operating home and outdoor retail brands, including the Backcountry acquisition.
No cert resources or costs were provided for this listing; prioritize demonstrated causal ML and decision-systems work.
Public-domain labor data — prepare examples for 2–3 of these.
Website: contactout.com
Public cache only — not an employee review.
Senior Machine Learning Engineer, Causal & Decision SystemsCscgeneration-2Austin, TX What This Role Involves The role involves building systems that estimate causal responses and quantify uncertainty, choosing actions based on this information, generating useful insights from observed outcomes, updating policies accordingly, evaluating challenger policies, and deploying within set guardrails. You will work on projects such as estimating the value of a challenger policy before full deployment or optimizing economic outcomes while respecting various constraints. What You'll Do 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 Qualifications We are looking for a candidate with experience in causal inference, machine learning, and decision-making systems. Knowledge of uncertainty estimation, policy optimization, and experimentation is essential. A background in econometrics or similar fields would be beneficial. About Cscgeneration-2 CSC Generation is dedicated to building closed-loop decision systems that leverage machine learning for smarter consumer business operations. Our focus areas include pricing, inventory management, promotions, marketing, and assortment optimization. We aim to develop robust systems capable of answering complex questions about cause and effect in dynamic business environments. Next Steps To apply, complete your application directly on this page, or you'll be redirected to the employer's application platform to finish submitting there.
Generated for personal interview prep · 2026-08-15 UTC · getajob.ai