Senior Data Scientist

About the role

Grasp the opportunity to apply data science to the physical world of manufacturing! We are looking for a Senior Data Scientist passionate about applying machine learning, statistics, experimentation, and optimization techniques to solve complex business problems across manufacturing, operations reliability, supply chain, and product quality domains.

Responsibilities

• Collaborate with manufacturing engineers, maintenance teams, and operations leaders to understand the challenges and opportunities of our manufacturing processes.
• Analyze manufacturing process data, sensor/IoT data, and quality metrics to uncover actionable insights.
• Design, execute, and analyze online and offline experiments, including A/B testing, causal inference, and counterfactual analysis, to evaluate the impact of data science solutions on business outcomes.
• Design, develop, and evaluate machine learning and deep learning models to solve forecasting, optimization, reliability, anomaly detection, and decision-support problems.
• Design and implement statistical process control methods and anomaly detection techniques to proactively address quality issues in the manufacturing process.
• Own the end-to-end model lifecycle, including feature engineering, training, validation, deployment, monitoring, retraining, and continuous improvement.
• Collaborate with software engineers, ML engineers, and data engineers to productionize models and integrate AI capabilities into business workflows.
• Demonstrate the ability to apply data science and machine learning techniques across multiple domains (e.g., manufacturing, supply chain, pricing, logistics), abstracting core patterns, and adapting solutions to new problem spaces.
• Translate ambiguous business problems into scientific approaches and influence stakeholders through data-driven recommendations.
• Develop analytical visualizations and communicate findings through dashboards, notebooks, and presentations that drive business decisions.
• Mentor junior team members and contribute to data science standards, reusable patterns, and best practices.

Requirements

• 5+ years of experience solving problems through statistical modeling, applied machine learning, and data analysis.
• Strong proficiency in programming environments and languages, specifically Python (e.g. pandas, scikit-learn, etc.) and R.
• Deep understanding of machine learning, statistical modeling, time series forecasting, optimization, anomaly detection, and experimentation methodologies.
• Experience applying machine learning and deep learning techniques using frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with experimentation and causal inference methods, including A/B testing, quasi-experimental designs, and counterfactual analysis.
• Experience communicating insights using Power BI or Python-based visualization libraries such as Plotly and Matplotlib.
• Experience with modern cloud platforms and data architectures, including AWS, Azure, Snowflake, and Databricks.
• Experience working with large datasets of manufacturing, quality, and sensor/IoT data.
• Excellent problem-solving skills and ability to translate business problems into data science solutions.
• Communicate findings clearly to both technical and non-technical stakeholders, providing recommendations that drive measurable improvements.