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
