Principal Data Scientist

SS Supply Chain Solutions; Bengaluru; Vor Ort; INR 1.500.000 - 2.500.000; Vollzeit

Zusammenfassung

SS Supply Chain Solutions is seeking a Lead/Principal Data Scientist specializing in Forecasting & Decision Science in Bengaluru. The ideal candidate will have over 7 years of experience building scalable data science solutions with a strong proficiency in Python, predictive analytics, and machine learning. Responsibilities include developing forecasting models, supporting deployment, and mentoring junior data scientists. The role requires a solid understanding of time series analysis, SQL, and stakeholder management skills.

Qualifikationen

• Strong proficiency in Python for data analysis and model development.
• Experience in predictive analytics and time series forecasting.
• Ability to manage ambiguity and drive discussions with stakeholders.

Aufgaben

• Build and deliver predictive analytics and forecasting solutions.
• Develop robust models for inventory analytics and business performance forecasting.
• Work closely with engineering and data teams.

Kenntnisse

Python

Predictive Analytics

Time Series Forecasting

Machine Learning

Statistical Analysis

Ausbildung

7+ years in Data Science or Machine Learning

Tools

SQL

Docker

FastAPI

MLflow

Jobbeschreibung

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