Senior Manager, Data Platform Engineering

Nova Biomedical: One Global Brand. One Vision. Together under one name. Advanced Instruments and Nova Biomedical are now united under one brand, Nova Biomedical, marking a major milestone in our journey to deliver greater value to our customers. By combining our strengths, we are accelerating innovation, supporting critical workflows, and delivering world-class service across the biopharmaceutical and clinical markets.

Driven by innovation and guided by our core values — Customer Centricity, Ingenuity, Ownership & Accountability, Collaboration, and Integrity — Nova creates solutions that make a global impact. At Nova, candidates will find opportunities to grow, contribute, and help build a culture focused on transparency, sustainability, effective communication, and employee engagement.

The Sr Manager, Data Platform Engineering will lead Nova's data analytics and data science capability. The successful candidate will have a strong data science background first, while also showing the ability to grow and oversee data analytics and reporting. This role is fundamentally responsible for two areas: helping build trusted enterprise insights (data analytics) while also advancing predictive and prescriptive methods and core machine learning models/algorithms (data science) to enable the business, improve customer experience, generate product insights, and expand product performance via models deployed within or external to the product.

• Lead a high-impact data science function at the center of Nova's digital, data, and AI transformation.
• Create trusted analytics and modeling capabilities that can influence decisions across the enterprise.
• Build the bridge between immediate insight needs and a modern, scalable data foundation.
• Create the foundation for data science capabilities that will directly support product innovation.

• →Enterprise role and technical expert leading data science and analytics across priority business domains (workforce, customer, and product)
• →Build and manage a roadmap for trusted reporting, self-service analytics, data science, and advanced analytics use cases.
• →Manage internal employees and external consultants, including data scientists, analysts, and report developers.
• →Establish standards for analytical methods, dashboard design, metric definitions, model validation, documentation, and insight delivery.
• →Partner with business leaders to identify high-value opportunities and convert ambiguous questions into prioritized analytical work.
• →Oversee forecasting, classification, optimization, anomaly detection, statistical analysis, experimentation, and model validation efforts.
• →Ensure analytics outputs align with governance, data quality, regulatory, and source-of-truth expectations.

• Master’s degree in data science, statistics, analytics, engineering, computer science, business analytics, or a related field; PhD preferred.
• Significant (10+ yrs) experience in analytics, BI, data science, applied machine learning, and enterprise insight delivery.
• Experience managing technical analytics teams, consultants, or cross-functional analytical programs.
• Strong knowledge of statistical analysis, data visualization, forecasting, model validation, business performance measurement, and dashboarding.
• Familiarity with modern data platforms, governed semantic/data models, and enterprise reporting environments.
• Strong executive communication, stakeholder management, and prioritization skills.

• Experience in medical device, diagnostics, life sciences, regulated manufacturing, or healthcare environments.
• Experience harmonizing analytics across ERP, CRM, data lake/lakehouse, IoT device data, complaints and field data.
• Experience building analytics operating models, metric registries, or self-service analytics programs.

• Analytics and data science work is prioritized around measurable business impact.
• Reporting trust improves through clearer definitions, governance, documentation, and adoption.
• Data science use cases generate practical recommendations, measurable value, and reusable methods.