Principal Data Scientist

About the Role

We are looking for a Lead/Principal Data Scientist – Forecasting & Decision Science with 7+ years of experience in building scalable, business-impacting data science solutions across predictive analytics, time series forecasting, machine learning, and applied AI .

This role is ideal for someone who is strong in Python, forecasting, advanced analytics, and enterprise-grade model development , and can translate complex business problems into practical, production-ready solutions. The candidate should be comfortable working across the full lifecycle of a data science solution — from problem framing and exploratory analysis to model development, deployment support, business validation, and continuous improvement .

A strong background in forecasting and predictive analytics is essential. Exposure to supply chain / operations use cases, decision science, or agentic AI systems will be an added advantage.

What You’ll Do

• •Build and deliver predictive analytics and forecasting solutions for business-critical use cases
• •Develop robust models for:
• •Demand Forecasting
• •Sales Forecasting
• •Inventory Analytics
• •Supply / Operations Planning
• •Business Performance Forecasting

• •Work extensively with time series and sequential data , including trend / seasonality modeling, lag and rolling features, forecast validation, backtesting, and performance improvement
• •Design and implement machine learning models for structured and semi-structured business data
• •Perform exploratory data analysis, feature engineering, model evaluation, and hypothesis-driven analysis
• •Translate business problems into analytical frameworks, model approaches, features / assumptions, and success metrics
• •Build clean, modular, production-friendly Python solutions
• •Work closely with business, product, engineering, and data teams to ensure solutions are practical, scalable, and business-aligned
• •Support deployment and integration of models into enterprise applications / APIs
• •Mentor junior team members and contribute to solution reviews, model quality, and best practices

Must Have

• •7+ years of relevant experience in Data Science, Machine Learning, Predictive Analytics, or Applied AI
• •Strong proficiency in Python for data analysis, model development, and solution engineering
• •Strong hands-on experience in:
• •Predictive Analytics
• •Time Series Forecasting / Time Series Modeling
• •Machine Learning model development
• •Statistical analysis and feature engineering
• •Regression / classification / forecasting use cases

• •Strong understanding of time series and sequential data , including:
• •Trend / seasonality analysis
• •Lag features / rolling features
• •Forecast validation
• •Backtesting
• •Forecast error analysis and performance tuning

• •Strong hands-on experience in time series forecasting, predictive analytics, and machine learning , with proficiency in relevant Python-based libraries, frameworks, and model development workflows
• •Strong understanding of core data science concepts such as:
• •Supervised / unsupervised learning
• •Model validation and error analysis
• •Feature selection / importance
• •Bias-variance trade-offs
• •Experimentation and hypothesis-driven analysis

• •Good understanding of SQL and working with large, structured datasets
• •Experience building clean, modular, and production-friendly code
• •Strong problem-solving and analytical thinking skills
• •Ability to independently convert business problems into:
• •Analytical frameworks
• •Model approaches
• •Features / assumptions
• •Metrics and success criteria

• •Strong stakeholder management and business communication skills , with the ability to work closely with business, product, and engineering teams to understand requirements, align on solution approach, and clearly communicate model outputs, assumptions, and recommendations
• •Ability to manage ambiguity, drive discussions with cross-functional stakeholders, and translate business asks into structured analytical solutions
• •Basic understanding of:
• •Docker / containers
• •Packaging and deployment of Python services / models
• •Linux / command-line basics
• •API integration concepts

Ability to mentor junior data scientists and review their approach / outputs

Good to Have

• •Experience in supply chain / operations domain , especially in one or more of:
• •Demand Forecasting
• •Inventory Analytics
• •Supply Planning
• •Production / Operations Analytics
• •Logistics / Distribution Analytics

• •Exposure to optimization / decision-support systems (not mandatory, but beneficial)
• •Familiarity with:
• •FastAPI / Flask
• •Git / version control
• •MLflow / experiment tracking
• •Airflow / workflow orchestration

• •Exposure to cloud environments such as AWS / Azure / GCP
• •Understanding of MLOps concepts such as:
• •Model packaging
• •Deployment workflows
• •Monitoring / retraining pipelines

• •Exposure to LLMs / Generative AI / Agentic Systems , including concepts such as:
• •Prompt engineering
• •RAG / context-aware AI systems
• •Tool calling / orchestration
• •Multi-agent workflows

• •AI-assisted analytics / decision support
• •Experience working on enterprise-scale analytics or decision intelligence platforms