Lead Data Scientist

The Lead Data Scientist (Focus: Commercial) is the primary data science partner to Sensata's commercial organization: Marketing, Sales, Revenue Operations, and Commercial Finance. This role builds machine learning, forecasting, and applied optimization models that improve how Sensata prices, sells, prioritizes customers and leads, and forecasts performance. The role also supports enterprise data science needs as priorities arise.

General Responsibilities
•    Oversees developing innovative product solutions to continuously drive value for the customer
•    Leads development project teams by defining data needs
•    Advises on insights to customer challenges
•    Identify valuable data sources and efficient collection processes
•    Analyze large data sets to discover trends and value driven insights
•    Leads predictive models and algorithms
•    Oversees development processes and tools to analyze model performance and data accuracy
•    Communicate complex findings and ideas using data visualization techniques

Experience / Qualifications
• A university degree required (i.e. Bachelors degree) or equivalent relevant work experience.
• Must be a team player able to work in a fast-paced environment with demonstrated ability to handle multiple competing tasks and demands
• Strong communication skills; oral, written and presentation
• Strong organization, planning and time management skills to achieve results
• Strong personal and professional ethical values and integrity
• Holds self-accountable to achieving goals and standards
• Proficient in Microsoft Office programs (Outlook, Word, PowerPoint, and Excel)
• Strong interpersonal & collaboration skills to work effectively with all levels of the organization including suppliers and/or external customers

Additional Responsibilities:

• Build and deploy models across the commercial lifecycle, including lead and opportunity scoring, customer propensity and churn, next-best-offer, and customer segmentation.
• Develop pricing analytics and decision models, including price elasticity, deal and quote guidance, and margin optimization at the product and customer level.
• Build forecasting models for market performance, revenue, bookings, and pipeline, combining internal commercial data with external market and peer data.
• Apply practical optimization methods to commercial and operational decisions such as scheduling, resource allocation, and pricing scenarios.
• Translate business questions from Marketing, Sales, Revenue Operations, and Commercial Finance into well-scoped data science problems, and explain the results in clear, decision-ready terms.
• Deliver ad hoc analyses and models for enterprise priorities outside the commercial organization as needed.
• Set standards for validating models, including testing, backtesting, monitoring, and retraining, so models stay accurate after launch.
• Partner with AI Engineers and platform teams to put models into production and embed them in business workflows and systems.
• Evaluate third-party analytics and AI solutions to validate performance claims and support technology decisions.

Additional Qualifications:

• 7+ years of experience in data science, machine learning, or applied statistics, ideally supporting sales, marketing, pricing, or commercial finance teams.

• Strong expertise in classification and propensity modeling, time-series forecasting, regression and causal analysis, and experimentation.

• Working knowledge of applied optimization methods (e.g., linear or mixed-integer programming) for pricing, scheduling, or allocation problems.

• Experience with commercial data sources such as CRM (e.g., Salesforce), ERP, pricing, and quoting systems.

• Strong proficiency in Python and SQL; experience with Azure ML, Databricks, or similar platforms preferred.

• Proven ability to explain complex models to executives and non-technical stakeholders and drive adoption.

• Advanced degree in Data Science, Statistics, Economics/Econometrics, Applied Mathematics, Computer Science, or a related quantitative field preferred

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