Senior ML Scientist 2025PX02004 MCA-04MCAF-59838
Senior ML Scientist 2025PX02004 MCA-04MCAF-59838
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Job Description
Job Summary
We seek a Senior ML Scientist to drive innovation in AI MLbased dynamic pricing algorithms and personalized offer experiences. This role will focus on designing and implementing advanced machine learning models including reinforcement learning techniques like Contextual Bandits Qlearning SARSA and more. By leveraging algorithmic expertise in classical ML and statistical methods you will develop solutions that optimize pricing strategies improve customer value and drive measurable business impact.
Job Responsibilities
Algorithm Development: Conceptualize design and implement stateoftheart ML models for dynamic pricing and personalized recommendations.
Reinforcement Learning Expertise: Develop and apply RL techniques including Contextual Bandits Qlearning SARSA and concepts like Thompson Sampling and Bayesian Optimization to solve pricing and optimization challenges.
AI Agents for Pricing: Build AIdriven pricing agents that incorporate consumer behaviour demand elasticity and competitive insights to optimize revenue and conversion.
Rapid ML Prototyping: Experience in quickly building testing and iterating on ML prototypes to validate ideas and refine algorithms.
Feature Engineering: Engineer largescale consumer behavioural feature stores to support ML models ensuring scalability and performance.
CrossFunctional Collaboration: Work closely with Marketing Product and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact.
Controlled Experiments: Design analyze and troubleshoot A/B and multivariate tests to validate the effectiveness of your models.
Essential Skills
8 years in machine learning 5 years in reinforcement learning recommendation systems pricing algorithms pattern recognition or artificial intelligence.
Expertise in classical ML techniques (e.g. Classification Clustering Regression) using algorithms like XGBoost Random Forest SVM and KMeans with handson experience in RL methods such as Contextual Bandits Qlearning SARSA and Bayesian approaches for pricing optimization.
Proficiency in handling tabular data including sparsity cardinality analysis standardization and encoding.
Proficient in Python and SQL (including Window Functions Group By Joins and Partitioning).
Experience with ML frameworks and libraries such as scikitlearn TensorFlow and PyTorch
Knowledge of controlled experimentation techniques including causal A/B testing and multivariate testing.
Nice to Haves
Background Check required
No criminal record
Others
Work Timing: Regular Hours Monday to Friday 9am to 5pm
Work Timezone: Global Timezone (GMT 0500) Eastern Time Toronto (EST)
Reporting Location: Markham
Hours per Week: 40
Machine learning Python SQL pricing algorithms artificial intelligence KMeans
Employment Type
Full Time
