Senior Machine Learning Engineer, Causal & Decision Systems
TLDR
Build closed-loop decision systems that estimate causal response, quantify uncertainty, and automate pricing and other commercial decisions.
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.
Interview Process
- Recruiter Screen: A conversation with our recruiting team to cover your background, the role, and mutual fit.
- 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.
- In-Person Interview: A final on-site visit at our Toronto office to meet the broader team and connect with key stakeholders.
- Reference Checks: Conducted in parallel with the final stages where possible.
- Offer: We move quickly for the right candidate.
Interview process is subject to change. Any updates will be shared promptly and clearly.
Please Note
CSC Generation is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law.
The CSC Generation family of brands is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact hrbenefits@cscshared.com.
For Ontario applicants, please note that this posting is for an existing vacancy.
CSC Generation will conduct an exhaustive background check including verifying dates of employment directly with your former employer.
Benefits
Equity Compensation
RRSP matching
Health Insurance
Comprehensive health, dental, and vision coverage
Cross-brand employee discounts
access to cross-brand employee discounts across the CSC Generation portfolio
Paid Time Off
CSC Generation is a dynamic company that acquires and transforms retail and ecommerce businesses to enhance growth. With a diverse portfolio of brands, we offer over 325,000 premium products, primarily focusing on kitchen and culinary goods, while leveraging innovative technology to create engaging shopping experiences for millions of customers.
- Founded
- Founded 2016
- Employees
- 500+ employees
- Industry
- Internet Software & Services