Role overview

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 (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans
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

Responsibilities

Conceptualize, design, and implement state-of-the-art ML models for dynamic pricing and personalized recommendations
Develop and apply RL techniques, including Contextual Bandits, Q-learning, SARSA, and concepts like Thompson Sampling and Bayesian Optimization
Build AI-driven pricing agents that incorporate consumer behavior, demand elasticity, and competitive insights to optimize revenue and conversion
Experience in quickly building, testing, and iterating on ML prototypes to validate ideas and refine algorithms

About the company

MDA Edge

Our objective is to establish an exceptional ecosystem by connecting individuals, technology, and prospects through the application of human intelligence.MDA Edge is a process-oriented company and our expertise lies in providing comprehensive Workforce Solutions, specifically focusing on Contingent Staffing, Bulk/Project Staffing, RPO/KPO/BPO, and Direct Hire services. We cater to a wide range of industries, including Infrastructure Consulting, Engineering Consulting, IT Consulting, Healthcare, Life Sciences, Pharmaceutical Consulting, Consumer Goods, Education, Transportation & Logistics, Media & Entertainment, Telecom, BFSI, Manufacturing, Utilities & Energies, and Corporate Recruitment. Moreover, we have developed a dedicated focus on Government Consulting.Our company prides itself on a meticulous approach, ensuring that we meet your specific needs. We have established a robust network of highly skilled professionals who are readily available to fulfill your requirements. With a strong commitment to excellence, we consistently deliver exceptional results for clients in India, Denmark, France, Germany, Ireland, Japan, Spain, Thailand, the USA, Canada and Mexico, with ongoing expansion efforts to serve more new regions in the near future.We consistently uphold the highest standards of quality by providing resources, time, and materials to design, implement, and support efficient operations for organizations. Our track record of measurable accomplishments demonstrates our commitment to cultivating a balanced work and societal ecosystem.Our continuous growth, successful customer engagements, and strong customer retention exemplify our achievements. Furthermore, our passion lies in streamlining complex business processes through the application of suitable technology, which has been integral to our success.We extend an invitation to join our dynamic workplace, offering rapid growth opportunities, excellent employee benefits, and a positive work-life balance.

Company details

Company typeScaleup

Company size201 - 500

Links

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Job description

Job 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.
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 (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.

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, 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 behavior, 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 behavioral 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.