Lead Data Scientist

Key Responsibilities

AI/ML Solution Development

• Design and develop scalable ML/AI models for: Elasticity estimation, Demand forecasting, Forecast attribution to drivers, Time-series anomaly detection, Simulation and constrained optimization systems.
• Build robust statistical and machine learning solutions using techniques such as: Linear regression, Gradient boosting, Deep learning, Probabilistic forecasting, Hierarchical modelling, Causal inference, Bayesian methods, MILP
• Reinforcement learning (preferred)
• Foundation models / GenAI applications (good to have)

Product & Platform Innovation

• Contribute to the architecture of an enterprise AI SaaS platform with focus on: Scalability, Explainability, Configurability, Multi-tenancy, Plug-and-play AI modules.
• Drive rapid experimentation and translate research into production-ready solutions.
• Develop reusable AI frameworks, feature engineering systems, validation pipelines, and experimentation tooling.
• Support model deployment and lifecycle management in production environments.
• Work with structured and unstructured large-scale datasets from multiple sources (transactional systems, external datasets, digital platforms, etc.)

Leadership & Collaboration

• Lead and mentor a team of data scientists and ML engineers.
• Drive innovation by evaluating new modeling techniques, data sources, and tools to enhance solution capabilities
• Collaborate closely with Product, Engineering, Solutions, and Customer Success teams to define AI roadmap, technical requirements and measurable success criteria
• Work directly with customers to understand business challenges and convert them into scalable AI solutions.
• Drive best practices in: ML system design, MLOps, Code quality, Experiment tracking, AI governance, Explainability

Business Impact

• Deliver measurable improvements in model accuracy, planner productivity, and business KPIs.
• Help shape the AI roadmap for strategic enterprise products in the CPG domain.
• Contribute to patents, research initiatives, and innovation programs.

Requirements

• Bachelor's/Master's/PhD in Computer Science, Statistics, Mathematics, Economics, Operations Research, or related field.
• 6–10+ years of experience in Data Science/ML roles, preferably in enterprise SaaS or CPG/Retail domains.
• Strong expertise in: Time-series analysis, Machine learning, Statistical modelling, Python ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow, Prophet, Stan/PyMC, etc.)
• Experience building production-grade ML systems and scalable pipelines.
• Strong understanding of ML system design and software engineering principles.
• Experience with cloud platforms (Azure/AWS/GCP) and distributed systems.
• Excellent problem-solving and communication skills.

Preferred Qualifications

• Experience in CPG/Retail forecasting or Revenue Growth Management.
• Exposure to GenAI/LLM-based applications and AI agents.
• Experience with MLOps tools, orchestration systems, and real-time inference pipelines.
• Published research, patents, or participation in AI competitions/hackathons.

What Makes This Role Exciting

• Opportunity to build industry-leading AI products solving real business problems at global scale.
• Work with rich enterprise datasets and direct customer feedback loops.
• High ownership and autonomy in shaping AI architecture and product direction.
• Strong innovation culture with opportunities for research, patents, and experimentation.
• Collaborate with a highly technical and business-oriented team passionate about transforming the CPG industry using AI.