Role: Lead Data Scientist (Demand Forecasting - Supply Chain) (13+ yrs)
9–12 years of hands-on experience in data science or machine learning — with a strong emphasis on Python-based ML engineering in production environments
3+ years of experience with time-series forecasting or supply chain analytics in a commercial context
Demonstrated experience building end-to-end ML pipelines from raw tabular data through model output and reporting — not just notebook prototyping
Experience working in cross-functional teams with stakeholders across business, IT, and analytics; ideally in a consulting or professional services environment
Track record of delivering high-quality, well-documented, reviewable code in a team setting
Expert-level Python: scikit-learn, pandas, numpy, scipy, joblib — able to write production-grade, optimised code for large datasets
Deep hands-on experience with ensemble methods: gradient boosting (GBM, XGBoost, LightGBM) and Random Forest — including hyperparameter tuning and performance diagnostics
Proficiency in quantile regression and probabilistic forecasting: building tree-level percentile prediction intervals, measuring PI coverage (Winkler score, pinball loss), and detecting quantile crossing violations
Strong statistical skills: KS 2-sample tests, ACF/PACF analysis, change-point detection, IQR outlier detection, Pearson/Spearman correlation
Proficiency with SQL for data extraction, transformation, and validation
Familiarity with version control (Git), experiment reproducibility (SEED management, config-driven pipelines), and collaborative development workflows
Master's degree or PhD in Data Science, Statistics, Computer Science, Machine Learning, Operations Research, or a related quantitative field
Bachelor's degree with equivalent industry experience in a quantitative discipline considered
Experience with intermittent demand modelling: Croston method, SBA, ADI and CV² classification for routing parts to appropriate forecast models
Experience with reconciliation frameworks: bottom-up and top-down forecast reconciliation, MinT reconciliation, hierarchical coherence
Familiarity with MLflow, DVC, or equivalent tools for experiment tracking and pipeline orchestration
Experience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) for scalable model training and deployment
Knowledge of S&OP processes, IBP (Integrated Business Planning), and multi-echelon inventory theory
Experience building user-facing analytical tools or dashboards — ideally with some exposure to full-stack data product development
Contributions to open-source ML projects or published work in forecasting, supply chain analytics, or applied ML
Deepak Singh
Technical Team Lead
Email: Dsr.itr...@gmail.com
LinkedIn: www.linkedin.com/in/deepak-singh-rajput-8a7b6a21a/
221 River St 9th floor Hoboken, NJ 07030