Role overview
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
For applications and inquiries, contact: hirings@openkyber.com