Senior ML Scientist
4 weeks ago
Salary range: 500 280.00/month (Marketplace Pay Rate)
Summary:
- 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.
Skills a must on resume:
- Machine Learning pricing algorithms
- Uplift Modeling
- Bayesian Optimization
- Multi-Armed Bandits
- Contextual Bandits
- Pricing Optimization
Requirements:
- 10 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.
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.
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