Senior Machine Learning Engineer, Causal & Decision Systems

CSC-Generatio; Toronto; On-site; CAD 120,000 - 160,000; Full time

Job summary

CSC Generation invites applications for a role focused on building scalable ML systems that estimate causal effects, quantify uncertainty, and optimize business decisions in a portfolio of commercial problems.

You will work across causal inference, experimentation, bandits, and policy learning, with emphasis on deploying reliable, data-driven decisions in production environments.

Qualifications

• Strong experience in machine learning and statistical modeling.
• Experience with causal inference and experimentation.
• Background in decision systems such as pricing, advertising, or marketplaces.
• Familiarity with bandits, reinforcement learning, optimization or active learning.
• Experience with uncertainty estimation and counterfactual evaluation.
• Proficient in Python, SQL, and handling large behavioral datasets.

Responsibilities

• Develop causal and heterogeneous treatment-effect models.
• Estimate uncertainty and calibrate models.
• Work on contextual bandits, active learning, or sequential decision-making.
• Lead policy learning and constrained optimization.
• Contribute to production ML systems, monitoring, and automated deployment.
• Collaborate across teams to ensure decisions improve the business.

Skills

ML modeling

Causal inference

Experimentation

Pricing/advertising/marketplaces

Bandits/RL/Optimization

Uncertainty estimation

Counterfactual evaluation

Production ML systems

Python

SQL

Large behavioral datasets

Job description

The Role

You will help build systems that:

**estimate causal response + quantify uncertainty → choose actions → generate useful information → observe outcomes → update policies → evaluate challengers → 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 Work On

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.

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 CSCGenerationfamily 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 [e-mail].

For Ontario applicants, please note that this posting is for an existing vacancy.