Data Scientist (Lead / Manager)

Role: 1.) Lead Data Scientist

• Level: Lead / Principal Individual Contributor
• Experience: 9 12 years in data science, machine learning, or applied statistics
• Domain: Demand Forecasting, ML Engineering

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

• 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.

Role: 2.) Senior Manager, Data Science

• Level: Senior Manager (Individual Contributor + People Management)
• Location: United States (Remote-friendly with quarterly travel)
• Experience: 12 15 years in data science, machine learning, or quantitative analytics
• Team: Leads a team of 3 6 data scientists across the US and offshore
• Domain: Supply Chain, Demand Forecasting, Operations Analytics

We are looking for a Senior Manager of Data Science to lead the end-to-end design, development, and deployment of advanced machine learning solutions for supply chain demand forecasting and root cause analysis. This is a hands-on leadership role you will architect the analytical framework, guide a cross-functional team of data scientists, and serve as the primary technical interface with senior stakeholders.

Required Qualifications

• 12 15 years of progressive experience in data science, machine learning, or quantitative analytics with at least 4 years in a lead or management role
• Proven track record delivering end-to-end ML pipelines in production environments from raw data through model deployment and monitoring
• Hands-on experience with demand forecasting, supply chain analytics, or operations research in an industrial, manufacturing, or distribution context
• Demonstrated experience leading cross-functional analytics teams, including offshore or distributed team members
• Experience presenting complex analytical findings to C-level and VP-level stakeholders with measurable business impact

Technical Skills

• Expert-level proficiency in Python: scikit-learn, XGBoost, LightGBM, statsmodels, pandas, numpy, scipy
• Deep expertise in ensemble methods gradient boosting (GBM, XGBoost, LightGBM) and random forest variants, including quantile regression forests
• Proficiency in probabilistic forecasting: quantile regression, prediction interval construction and calibration, Winkler scoring, pinball loss
• Strong statistical foundation: hypothesis testing, KS tests, distribution shift detection, time-series analysis (ACF, PACF, change-point detection)
• Experience with feature engineering for time-series and supply chain data: lag features, rolling statistics, Fourier encoding, interaction terms
• Proficiency with experiment tracking and MLOps tooling (MLflow, DVC, or equivalent); familiarity with CI/CD for ML pipelines
• Ability to write and review production-quality Python code; experience with SQL for data extraction and transformation
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud Platform) for model training, deployment, and scheduled execution