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 QualificationsExperience9–12 years of hands-on experience in data science or machine learning — with a strong emphasis on Python-based ML engineering in production environments3+ years of experience with time-series forecasting or supply chain analytics in a commercial contextDemonstrated experience building end-to-end ML pipelines from raw tabular data through model output and reporting — not just notebook prototypingExperience working in cross-functional teams with stakeholders across business, IT, and analytics; ideally in a consulting or professional services environmentTrack record of delivering high-quality, well-documented, reviewable code in a team settingTechnical SkillsExpert-level Python: scikit-learn, pandas, numpy, scipy, joblib — able to write production-grade, optimised code for large datasetsDeep hands-on experience with ensemble methods: gradient boosting (GBM, XGBoost, LightGBM) and Random Forest — including hyperparameter tuning and performance diagnosticsProficiency in quantile regression and probabilistic forecasting: building tree-level percentile prediction intervals, measuring PI coverage (Winkler score, pinball loss), and detecting quantile crossing violationsStrong statistical skills: KS 2-sample tests, ACF/PACF analysis, change-point detection, IQR outlier detection, Pearson/Spearman correlationProficiency with SQL for data extraction, transformation, and validationFamiliarity with version control (Git), experiment reproducibility (SEED management, config-driven pipelines), and collaborative development workflowsEducationMaster's degree or PhD in Data Science, Statistics, Computer Science, Machine Learning, Operations Research, or a related quantitative fieldBachelor's degree with equivalent industry experience in a quantitative discipline consideredPreferred QualificationsExperience with intermittent demand modelling: Croston method, SBA, ADI and CV² classification for routing parts to appropriate forecast modelsExperience with reconciliation frameworks: bottom-up and top-down forecast reconciliation, MinT reconciliation, hierarchical coherenceFamiliarity with MLflow, DVC, or equivalent tools for experiment tracking and pipeline orchestrationExperience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) for scalable model training and deploymentKnowledge of S&OP processes, IBP (Integrated Business Planning), and multi-echelon inventory theoryExperience building user-facing analytical tools or dashboards — ideally with some exposure to full-stack data product developmentContributions to open-source ML projects or published work in forecasting, supply chain analytics, or applied ML