Senior Machine Learning Engineer, Causal & Decision Systems

CSC Generation

. Build closed-loop decision systems using machine learning to operate consumer businesses more intelligently .

Posted 9/29/2026full-timeToronto • CanadaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building closed-loop decision systems using machine learning, with a focus on causal inference, uncertainty estimation, and production ML systems. Proficient in Python and SQL, capable of handling large behavioral datasets to drive measurable economic outcomes.

Highest-signal resume keywords

Machine LearningCausal InferenceProduction ML SystemsPythonSQL

ATS Keywords

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Hard Skills

Machine LearningStatistical ModelingCausal InferenceReinforcement LearningOptimizationActive LearningUncertainty EstimationCounterfactual EvaluationProduction ML SystemsBehavioral Data Analysis

Soft Skills

Exceptional Technical AbilityJudgment

Industry Keywords

Decision SystemsCausal Response EstimationContextual BanditsSequential Decision-MakingPolicy LearningChampion/Challenger SystemsExperimental Design

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Build closed-loop decision systems using machine learning to operate consumer businesses more intelligently
Develop systems for causal response estimation, uncertainty quantification, action selection, information generation, outcome observation, policy updates, challenger evaluation, and guarded deployment
Work across causal and heterogeneous treatment-effect modeling, uncertainty estimation and calibration, contextual bandits, active learning, sequential decision-making, policy learning, constrained optimization, counterfactual and off-policy evaluation, experimentation, champion/challenger systems, and production ML infrastructure
Produce measurable economic lift in controlled experiments
Build systems that generalize across businesses, learn from interventions, and safely automate commercial decisions
Own problems end to end, from framing and modeling through production deployment and evaluation
Report to the CTO
Participate in recruiter screening, virtual interview rounds, an in-person interview at the Costa Rica office, and reference checks

Requirements

What you’ll need

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
Proficiency with Python and SQL
Experience working with large behavioral datasets
Exceptional technical ability and judgment
Valid LinkedIn profile URL, or a credible reason and alternative evidence of professional background if no LinkedIn profile
Must attend a mandatory in-person interview before an offer

Benefits

Comp & perks

Primarily remote work
Private medical and life insurance
Additional paid time off
Monthly allowances and reimbursements
Employee discounts
Opportunities for professional growth