Job Description

About Company ::

Bristlecone is a supply chain and business analytics advisor, serving customers across a wide range of industries. Rated by Gartner as among the top ten system integrators in the supply chain space, we are uniquely positioned to solve contemporary business problems, with supply chain and analytics focus as our advantage. We have been a trusted partner and advisor to many leading, globally recognized companies such as Applied Materials, Exxon Mobil, Flextronics, LSI Logic, Mahindra, Motorola, Nestle, Palm, Qatar Petroleum, Ranbaxy, Unilever and Whirlpool and many others

About the Role

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

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