This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior ML Scientist (Optimization & Reinforcement Learning) based in India.
This is an opportunity to shape advanced AI solutions that optimize pricing and personalized customer experiences at scale.
You will design and deploy machine learning and reinforcement learning models that influence real-world pricing and recommendation decisions.
The role combines deep algorithmic expertise with experimentation, optimization, and practical business problem-solving.
You will work with techniques including Contextual Bandits, Q-learning, SARSA, Bayesian Optimization, and classical machine learning.
Your work will help improve revenue, conversion, customer value, and the effectiveness of dynamic pricing strategies.
You will collaborate closely with product, marketing, and sales stakeholders in a highly iterative, innovation-focused environment.
The position offers significant scope to prototype new ideas, turn research into production-ready solutions, and deliver measurable impact.
Accountabilities:
- Algorithm Development: Conceptualize, design, implement, and optimize advanced machine learning models for dynamic pricing, personalization, and recommendation use cases.
- Reinforcement Learning: Apply techniques such as Contextual Bandits, Q-learning, SARSA, Thompson Sampling, and Bayesian Optimization to solve complex pricing and optimization problems.
- AI-Powered Pricing Agents: Develop intelligent pricing agents that incorporate consumer behavior, demand elasticity, competitive signals, and other relevant factors to optimize revenue and conversion.
- Rapid Prototyping: Quickly develop, test, and iterate on machine learning prototypes to validate hypotheses, assess feasibility, and refine algorithms.
- Feature Engineering: Build and optimize large-scale consumer behavioral feature sets and feature stores that support scalable, high-performance machine learning applications.
- Experimentation: Design, analyze, and troubleshoot controlled experiments, including causal A/B and multivariate testing, to evaluate model effectiveness and business impact.
- Cross-Functional Collaboration: Partner with Product, Marketing, Sales, and other stakeholders to translate business objectives into effective ML solutions and measurable outcomes.
Requirements
- Experience: 8+ years of experience in machine learning, with 5+ years of hands-on experience in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, artificial intelligence, or closely related fields.
- Machine Learning Expertise: Strong knowledge of classical ML methods, including classification, clustering, and regression, with practical experience using algorithms such as XGBoost, Random Forest, SVM, and KMeans.
- Reinforcement Learning: Demonstrated expertise with Contextual Bandits, Q-learning, SARSA, Bayesian approaches, Thompson Sampling, Bayesian Optimization, and related optimization techniques.
- Data Expertise: Strong experience working with tabular data, including sparse datasets, cardinality analysis, standardization, encoding, and feature engineering.
- Programming: Proficiency in Python and SQL, including Window Functions, GROUP BY, JOINs, and partitioning.
- ML Frameworks: Hands-on experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, and PyTorch.
- Experimentation: Knowledge of controlled experimentation methodologies, including causal A/B testing and multivariate testing.
- Problem-Solving: Strong analytical and quantitative skills, with the ability to translate complex optimization and ML challenges into practical, scalable solutions.
- Collaboration: Excellent communication skills and the ability to work effectively with technical and non-technical stakeholders across multiple functions.
Benefits
- Opportunity to work on advanced machine learning, reinforcement learning, optimization, dynamic pricing, and personalization challenges.
- High-impact role with the opportunity to influence measurable business outcomes through AI-driven solutions.
- Exposure to large-scale consumer data, experimentation, and real-world ML applications.
- Collaborative environment with cross-functional interaction across Product, Marketing, Sales, and technical teams.
- Flexibility and working arrangements aligned with the partner company's policies and role requirements.
- Competitive compensation package based on experience, skills, and market alignment.
- Access to benefits and employee programs provided under the partner company's applicable employment policies.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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