Principal AI Data Science Engineer, Go-To-Market

About the Role

As Principal AI Data Science Engineer, Go-to-Market Analytics, you will apply statistical modeling, machine learning, and forecasting to customer, market, and commercial data. Your work will inform customer targeting, territory design, product launches, resource allocation, and growth planning across MiniMed. You will partner with commercial leaders to translate complex analysis into clear, actionable decisions. This role is based at our Northridge, CA headquarters and requires on-site work four days per week.

Key Responsibilities

• Develop, validate, monitor, and maintain predictive models and forecasts that assess growth opportunities, revenue potential, customer behavior, market dynamics, and competitive threats.
• Own customer targeting, prioritization, and tiering methodologies to focus commercial resources on the highest-value opportunities.
• Quantify market opportunities and evaluate business impact to support investment decisions and strategic initiatives.
• Translate analytical findings into recommendations that guide resource allocation, launch planning, territory design, and growth priorities.
• Partner with commercial leaders to improve forecasting accuracy and growth planning using historical performance, market trends, and leading indicators.
• Build early-warning intelligence to identify underperforming accounts, competitive share shifts, territory opportunities, market changes, adoption trends, and emerging commercial risks.
• Establish and maintain analytical methodologies that are accurate, explainable, well documented, and responsive to changing market conditions.

Minimum Qualifications

• Bachelor's degree and at least 7 years of relevant experience, or an advanced degree and at least 5 years of relevant experience.
• At least 5 years of experience in data science, analytics, business analytics, operations analytics, customer analytics, insights, or a related field, including ownership of complex, cross-functional initiatives.
• Experience using AI-enabled tools to accelerate analytical workflows, including data investigation, automation, dashboard development, data visualization, or insight generation.
• Proficiency in Python, R, or a similar programming language, with experience applying statistical or machine learning methods to large, complex datasets and business problems.
• Experience using SQL and modern analytics environments, including dbt, cloud data warehouses, and data visualization platforms.

Preferred Qualifications

• Master's degree or PhD in data science, analytics, statistics, or another relevant quantitative discipline.
• Experience applying natural language processing techniques to business problems.
• Experience deploying and maintaining analytical or machine learning models in cloud environments, including knowledge of MLOps practices such as version control, testing, monitoring, and model governance.
• Experience in the medical technology, medical device, or healthcare industry, particularly using customer, provider, market, or sales data to inform commercial strategy.
• Experience developing, validating, monitoring, and interpreting predictive models or forecasts, and translating outputs into actionable business recommendations.
• Experience applying analytics to commercial challenges such as customer segmentation and targeting, revenue forecasting, market opportunity sizing, or resource allocation.
• Experience managing the analytical lifecycle from problem definition and data preparation through implementation, documentation, performance monitoring, and stakeholder communication.
• Ability to communicate complex technical findings as clear, actionable recommendations for business leaders and cross-functional stakeholders.
• Ability to work independently and exercise sound judgment regarding data quality, privacy, responsible AI use, and model limitations.
• Ability to adapt quickly in a fast-paced, evolving environment as priorities and business needs change.