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This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist_ML based in India.
This role is a high-impact data science position focused on building and scaling machine learning solutions that directly influence pricing, demand forecasting, and revenue optimization in large-scale retail and e-commerce environments. You will work on end-to-end ML lifecycle development, from model design and experimentation to deployment and monitoring in production systems. The position involves solving complex business problems using advanced statistical methods and machine learning techniques, with a strong emphasis on real-world business impact. You will collaborate closely with engineering, product, and business teams to translate data into actionable pricing and demand strategies. This is a hands-on role where you will also contribute to MLOps practices, scalable pipelines, and production-ready ML systems. The environment is fast-paced, data-driven, and innovation-focused, offering exposure to modern AI technologies and large-scale retail datasets.
Accountabilities:
- Design, develop, and deploy machine learning models focused on pricing optimization, demand forecasting, promotion effectiveness, and revenue forecasting.
- Build and enhance statistical and ML models including demand forecasting, price elasticity, and inventory optimization solutions.
- Develop scalable ML pipelines and production-ready systems using frameworks such as Databricks and cloud-based MLOps platforms.
- Design, evaluate, and improve time series forecasting models, including advanced approaches such as transformer-based architectures.
- Analyze large-scale structured and unstructured retail datasets to generate actionable business insights and pricing strategies.
- Implement model monitoring, experimentation frameworks, and continuous improvement processes for ML systems.
- Collaborate with cross-functional teams including engineering, product, and business stakeholders to deliver scalable AI solutions.
- Mentor junior data scientists and contribute to best practices across data science and ML engineering teams.
- Communicate findings, insights, and recommendations clearly to both technical and non-technical stakeholders.
Requirements:
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
- 5+ years of experience in machine learning and data science roles.
- Strong foundation in probability, statistics, regression analysis, and machine learning algorithms.
- Advanced programming skills in Python, including libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow or similar frameworks.
- Strong experience with SQL and working with large-scale datasets and data warehouses.
- Hands-on experience with PySpark and distributed data processing systems.
- Experience building and deploying ML models using APIs and frameworks such as Flask or similar tools.
- Domain experience in retail, CPG, or e-commerce with expertise in demand forecasting, pricing, and promotion analytics.
- Experience with MLOps tools and platforms such as Databricks, AWS SageMaker, Azure ML, or Google Vertex AI.
- Strong communication skills with the ability to translate complex models into business insights.
Benefits:
- Competitive compensation aligned with senior data science expertise.
- Opportunity to work on large-scale AI and machine learning systems in retail and e-commerce domains.
- Exposure to advanced forecasting, pricing optimization, and cutting-edge ML techniques.
- Remote or flexible work arrangements depending on project needs.
- Strong career growth opportunities in AI/ML engineering and data science leadership.
- Access to modern cloud platforms, MLOps tools, and advanced AI infrastructure.
- Collaborative and innovation-driven work environment with cross-functional teams.
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