Lead Data Scientist (AI/ML)

We are hiring a Lead Data Scientist to be the primary technical engine of our supply chain demand forecasting and root cause analysis platform. This is a hands-on senior individual contributor role with significant ownership — you will implement, validate, and maintain the full ML pipeline, working closely with the US-based Senior Manager. Required Qualifications Experience • 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 Technical Skills • 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 Education • 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 Preferred Qualifications • 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