Senior Manager, Data Platform Engineering
About the role
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Responsibilities
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Requirements
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Skills
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Qualifications
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Benefits
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Pay
Salary Range: $200,000 to $240,000 yearly
We need to ensure we don't create sections with filler. All have content. We must preserve all substantive information: need to include everything from the posting: Company Overview, Job Summary, Why This Role Matters, Responsibilities bullet points, Qualifications, Preferred Experience, What Success Looks Like, Benefits And Growth. We need to decide where to place each piece. About the role: could include Company Overview + Job Summary + Why This Role Matters? Might be too much but okay. But we must not repeat job title. We need to ensure we don't lose any detail. Let's extract details: Company Overview: "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." Job Summary: "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." Why This Role Matters: "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." Responsibilities list: - 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. Qualifications: - 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. Preferred Experience: - 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. What Success Looks Like: - 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. Benefits And Growth: - Nova offers competitive benefits designed to support employee health, financial security, work-life balance, and professional growth. - Benefits vary by country and employment type; full-time employees can generally expect comprehensive health coverage, retirement savings options, paid time off, wellness initiatives, and resources for development. Pay: Salary Range: $200,000 to $240,000 yearly. Location: USA / Waltham, MA area preferred (but we should not create a heading for location unless we have a section; we could fold into About the role paragraph maybe). The instruction: do NOT put Job ID / requisition numbers as headings — fold minor metadata into a shortor drop it. Location is minor metadata; we can include in a short p maybe under About the role or separate paragraph. Since we have About the role section, we can include location there. Employment Type: Full-time (also minor metadata). Could include in About the role. Team: Data & AI (maybe also minor). Could include. We need to preserve ALL substantive information; location, employment type, team are substantive? They are metadata but still info. We should not drop them; we can fold into a short p. Thus About the role paragraph could include: Employment Type: Full-time. Location: USA / Waltham, MA area preferred. Team: Data & AI. Plus company overview, job summary, why role matters. We must not create sections with filler. All good. Now we need to decide which headings to use: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay. Possibly also "Preferred Experience" could be part of Requirements or Qualifications. But we have both Requirements and Qualifications headings; we need to decide what goes where. We could treat Requirements as the mandatory qualifications (Master's degree, 10+ yrs experience, managing teams). Qualifications could be the strong knowledge etc. Skills could be the strong executive communication etc. But there is overlap. Better: Use Requirements for the mandatory education/experience; Qualifications for the knowledge/familiarity; Skills for the communication etc. However the instruction says prefer a few substantive sections over many thin ones. So maybe combine into fewer sections: About the role, Responsibilities, Qualifications (covering education, experience, knowledge, skills), Benefits, Pay. But we need to follow the allowed headings list; we can omit some if not needed. The instruction: Use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It says e.g., meaning those are examples; we can use those headings but not required to use all. We should include a section only when posting has real specific content for it. So we can choose which headings to use.
We have content for About the role, Responsibilities, Qualifications (maybe combine education/experience/knowledge/skills), Benefits, Pay. Could also have Requirements separate but maybe not needed if we put all in Qualifications. However we have both Qualifications and Preferred Experience; we could put Requirements as the mandatory (education, experience, managing teams) and Qualifications as the knowledge/familiarity/skills. But that creates two sections.
Given the instruction to prefer few substantive sections over many thin ones, maybe we combine into About the role, Responsibilities, Qualifications (including education, experience, knowledge, skills), Benefits, Pay. That's 5 sections.
Let's see if we lose any detail: We need to include Preferred Experience and What Success Looks Like. Those could be part of Qualifications or maybe a separate section like "Preferred Experience" but not in allowed headings. Could fold into Qualifications as bullet points.
What Success Looks Like could be part of Responsibilities or a separate section like "Expectations". Not in allowed headings; maybe fold into Responsibilities or Qualifications. Could be part of Responsibilities as outcomes.
Thus we can incorporate.
Let's craft:
About the role
Employment Type: Full-time. Location: USA / Waltham, MA area preferred. Team: Data & AI. 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.
That's a long