Senior Machine Learning Engineer, Causal & Decision Systems

CSC Generation; Toronto; Hybrid; CAD 120,000 - 180,000; Full time

Benefits offered by this job

Health, dental, and vision coverage

RRSP matching

Paid time off

Cross-brand employee discounts

Job summary

CSC Generation in Toronto is seeking an experienced ML engineer to design and deploy closed-loop decision systems for pricing, inventory, promotions, and related business decisions across multiple brands. You will work on systems that estimate causal response, quantify uncertainty, select actions, observe outcomes, and deploy within guardrails.

You will collaborate with data science and engineering to build production ML that lifts measurable economic outcomes, with ownership from framing to

Qualifications

• Experience building ML models for pricing, inventory, or procurement in a production setting.
• Strong background in causal ML, experimentation, and policy learning.
• Proficiency in Python for data science and model deployment.
• Experience with large behavioral datasets and SQL querying.
• Ability to estimate uncertainty and calibrate models.
• Familiarity with counterfactual evaluation and off-policy analysis.

Responsibilities

• Design causal and heterogeneous treatment-effect models for business decisions.
• Develop uncertainty estimation, calibration, and robustness analyses.
• Implement contextual bandits, active learning, or sequential decision-making.
• Build policy learning and constrained optimization workflows for real-time decisions.
• Perform counterfactual and off-policy evaluation.
• Contribute to production ML infrastructure, monitoring, and automated deployment.

Skills

Machine learning

Causal inference

Bandits

Uncertainty estimation

Counterfactual evaluation

Python

SQL

Production ML

Experimentation

Large datasets

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 systemsProduction 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 experimentationRecommendation, 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

• 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

• 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.

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 [email protected] .

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.