Senior ML Scientist

Senior ML Scientist (Pricing Reinforcement Learning)

Mississauga, ON (Remote)

Contract

Mandatory Skills: ML Scientist (Pricing Reinforcement Learning),Python, Tableau, SQL (Knowledge)

Note: 1st round is technical round. If shortlisted for candidate, L2 round is coding round (It’s be 1hour 30minutes, Final round with End client)

Job Description:

We seek a Senior ML Scientist to drive innovation in AI ML based 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 Learning 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

Key Responsibilities

• Algorithm Development Conceptualize design and implement state-of-the-art 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 AI driven 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
• Cross Functional 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 AB and multivariate tests to validate the effectiveness of your models

Qualifications

• 8 years in machine learning 5 years in reinforcement learning recommendation systems pricing algorithms pattern recognition or artificial intelligence
• Expertise in classical ML techniques eg Classification Clustering Regression using algorithms like XGBoost Random Forest SVM and KMeans with hands on 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 AB testing and multivariate testing