Data Scientist Lead - Manufacturing Domain

Role Overview:

As a Data Scientist Lead in the manufacturing domain, you will spearhead the development and deployment of advanced analytical solutions designed to optimize industrial operations, supply chain efficiency, and predictive maintenance protocols.

You will work in close collaboration with cross-functional teams, including production engineers, supply chain managers, and executive stakeholders, to translate complex manufacturing challenges into scalable data products.

By leveraging cutting-edge techniques in machine learning and agentic AI, you will directly influence business outcomes by reducing downtime, improving yield, and driving data-driven decision-making across global manufacturing facilities.

Key Responsibilities:

• Architect and implement robust data models that transform raw sensor and operational data into actionable insights for plant managers to improve production throughput.
• Lead the end-to-end lifecycle of machine learning models, from initial hypothesis generation to production deployment, ensuring high model performance and reliability in industrial environments.
• Integrate agentic AI frameworks to automate complex decision-making processes, enabling autonomous responses to supply chain disruptions and equipment anomalies.
• Partner with engineering teams to refine data collection strategies, ensuring that high-quality data streams are available for real-time monitoring and long-term trend analysis.
• Communicate technical findings and strategic recommendations to non-technical stakeholders, bridging the gap between data science capabilities and operational business requirements.

Required Skillset:

• Demonstrated expertise in building and scaling machine learning models within a manufacturing or industrial IoT context, with a deep understanding of time-series data and predictive maintenance.
• Proven ability to design and implement agentic AI systems that interact with existing software ecosystems to perform autonomous tasks.
• Strong proficiency in data modeling and statistical analysis, with the ability to structure complex datasets to support high-stakes business intelligence.
• Exceptional communication skills, with the ability to articulate complex technical concepts to senior leadership and influence cross-functional project outcomes.
• High degree of adaptability to work effectively in a distributed, remote-first environment, maintaining strong collaboration across different time zones and plant locations.
• A solid academic foundation in Computer Science, Statistics, Engineering, or a related quantitative field, complemented by 2 - 5 years of hands-on experience in high-impact data science roles.