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

- Prospect 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.

📌 Lead Data Scientist (Bengaluru)
🏢 Asper.ai
📍 Bengaluru