Senior Machine Learning Engineer, Causal & Decision Systems

Job description

CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs. Reports to: CTO Location: Hybrid- Toronto, ON ### About the Role 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? ### What You'll Do 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. - **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 health, dental, and vision coverage; RRSP matching; paid time off; and access to cross-brand employee discounts across the CSC Generation portfolio. ### Interview Process 1. **Recruiter Screen:** A conversation with our recruiting team to cover your background, the role, and mutual fit. 2. **Virtual Interview Rounds:** Focused discussion with the hiring manager & deeper conversations with cross-functional engineering and data science collaborators covering technical depth, system design, and working style. 3. **In-Person Interview:** A final on-site visit at our Toronto office to meet the broader team and connect with key stakeholders. 4. **Reference Checks:** Conducted in parallel with the final stages where possible. 5. **Offer:** We move quickly for the right candidate. *Interview process is subject to change. Any updates will be shared promptly and clearly.* ### Please Note - Part of our interview process is a **mandatory in-person interview** with someone on our team prior to an offer. Candidates that are unwilling or unable to meet for an in-person interview will be removed from consideration immediately. - Due to a high volume of fraudulent applications, **you must share a valid LinkedIn profile URL in** **the application questions below to be considered**. If you do not have a LinkedIn profile, you must provide a credible reason in that field and supply alternative evidence of your professional background to verify your identity.