ML Scientist

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 hands-on experience in RL methods such as Contextual Bandits Q-learning 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 scikit-learn TensorFlow and PyTorch • Knowledge of controlled experimentation techniques including causal A/B testing and multivariate testing.

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, Q-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.

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, Q-learning, 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 large-scale 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 A/B and multivariate tests to validate the effectiveness of your models.

Educational Requirements

• The focused skills are Reinforcement learning, optimization techniques, pricing , Baysium , tabular ML, traditional ML and Classical AI models.
• AI-ML- Data Engineering + ML Principles
• ML/AI OOPS- Streamline
• MLOPS- Scalar pipelines, Drift detection, Model Registry, build Modular, module internal Libraries, Heary Python, Pyspark
• ML frameworks- TensorFlow, Theano, Scikit-learn, Caffe, Apache Mahout, Apache Spark, PyTorch, Amazon Sage Maker, Microsoft Cognitive Toolkit