Lead Data Scientist

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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