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About This Role
Associate Director, Analytics Transformation and AI Enablement
Facility: Digital \& IT
Location:Plainsboro, NJ, US
About the Department
The Finance \& Operations department brings insights and intelligence to inform decision making \& drives digitalization and business solutions to attain NNI goals. Finance \& Operations works closely across the organization to guide enterprise\-wide resource allocations, investment choices, drive core operations and develop insights to drive growth and operational excellence across the value chain while innovating for future capabilities. Our focus on innovation ensures we're constantly building future capabilities. We're responsible for regulating accounting, upholding workplace safety, managing our supply chain and sampling, supporting technological and data innovation, maintaining our facilities and assuring the integrity and completeness of all business transactions. At Novo Nordisk, you will have the opportunity to build a career in a global business environment. We encourage our employees to make the most of their talent, and we reward hard work and dedication with opportunities for continuous learning and personal development. Are you ready to maximize your potential with us?
The Position
The Associate Director, Analytics Transformation and AI Enablement is accountable for defining the data foundation that will power Novo Nordisk's US Commercial AI ambitions and for leading the continuous transformation of commercial analytics capabilities to ensure trusted, accessible, and modern insight delivery. Serving as a strategic leader and subject matter authority on AI\-ready data and analytics capabilities, this role defines the data requirements, quality standards, and integration pathways needed to support prioritized AI use cases across commercial operations \- while simultaneously optimizing Novo Nordisk's business intelligence platforms, governing enterprise metrics, streamlining reporting, and pioneering the next generation of AI\-augmented analytics engagement. By establishing the data foundation that enables AI to deliver reliable, high\-impact commercial decisions, and by elevating the analytics capability stack that commercial stakeholders rely on every day, this role plays a central part in positioning Novo Nordisk's US Commercial organization to realize the full value of its data and analytics investments \- accelerating AI readiness, modernizing analytics engagement, and ensuring commercial teams have the trusted insights they need to compete and win.
Relationships
The Associate Director, Analytics Transformation and AI Enablement reports to the Director / Senior Director, Data \& Analytics Strategy. This role maintains frequent interaction with leaders and stakeholders across Novo Nordisk Inc., including Enterprise Insights \& Analytics, Marketing, Sales, IT, Market Access and Advanced Analytics to ensure AI\-ready data foundations and analytics capabilities are aligned to commercial business needs and enabling effective, insight\-driven decision\-making.
Essential Functions
- AI Data Strategy \& Enablement
+ Define and own the commercial data foundation required to support prioritized AI use cases across US Commercial operations, ensuring alignment with NNI's enterprise AI roadmap
+ Establish clear data requirements, quality standards, and integration pathways that enable AI models — including generative and agentic applications — to deliver reliable, actionable commercial outputs
+ Partner with Advanced Analytics, DD\&IT, and enterprise AI teams to ensure commercial data assets are structured, governed, and accessible to power high\-impact AI initiatives
+ Identify and close data readiness gaps that may impede AI adoption, driving a continuous readiness assessment across commercial data domains
- Analytics Capabilities Transformation
+ Own and optimize Novo Nordisk's US Commercial business intelligence platforms, including Tableau and Power BI, ensuring they are performant, scalable, and aligned to evolving commercial needs
+ Lead the governance of enterprise metrics and reporting — defining standards, streamlining report inventories, and eliminating duplication to ensure commercial stakeholders operate from a trusted, single source of truth
+ Advance data and analytics cataloging to improve discoverability, transparency, and self\-service access across commercial users
+ Drive continuous improvement of the analytics capability stack, assessing emerging tools and platforms and guiding investment decisions
- Next\-Generation Analytics Engagement
+ Pioneer the adoption of AI\-augmented analytics capabilities, including GPT\-like conversational interfaces, modern augmented analytics platforms, and AI\-powered insight delivery, to transform how commercial stakeholders engage with data
+ Evaluate, pilot, and scale innovative analytics solutions that move the organization beyond traditional dashboarding toward more intelligent, embedded, and accessible insight experiences
+ Establish the vision and execution roadmap for the next generation of commercial analytics engagement, ensuring the organization remains at the leading edge of analytics capability
- Strategic Leadership \& Stakeholder Partnership
+ Translate enterprise AI and analytics ambitions into concrete data and capability roadmaps, ensuring strategic priorities are matched by executable delivery plans
+ Serve as a trusted advisor to Commercial, Enterprise Insights \& Analytics, Marketing, Market Access, and other commercial stakeholders on AI data readiness and analytics capability investment
+ Manage external vendor relationships and technology partnerships relevant to AI\-ready data and analytics capabilities, ensuring value delivery and alignment with NNI standards
+ Contribute to the broader Data \& Analytics Strategy agenda, advancing enterprise\-wide capability building and the continued evolution of NNI's commercial data and analytics function
Physical Requirements
Up to 10% overnight travel required.
Development of People
Supervisory. Ensure that reporting personnel have individual development plans (IDP), with annual goals and measurements that are consistent with the priorities of the business, and that interim reviews are held so that their work is focused on those priorities, and they understand their level of accountability for results and the measurement process. Ensure that the IDP forms include completed learning and aspiration plans and are in place for all reporting personnel to enable the achievement of goals and capability to assume increased levels of responsibility. Manage the application and communication of all Novo Nordisk policies, procedures, and Novo Nordisk Way.
Qualifications
- Bachelor's degree in a relevant field required (e.g., Business, Technology, Data, Analytics, Information Systems, Computer Science, or a related discipline); advanced degree preferred
+ 9\+ years of progressive experience in data strategy, analytics, or business intelligence, preferably within pharmaceutical, life sciences, or other highly regulated industries
+ 2\-3 years of people management experience preferred
- Demonstrated experience defining data requirements, quality standards, and integration pathways that enable AI and advanced analytics use cases at scale in a complex enterprise environment
- Deep expertise in modern business intelligence platforms, including Tableau, Power BI, or equivalent, with a proven track record of platform ownership, optimization, and governance
- Working knowledge of generative and agentic AI capabilities and the data, infrastructure, and governance requirements needed to support them responsibly at scale
- Demonstrated experience establishing and governing enterprise metrics, reporting standards, and data cataloging approaches to ensure trusted, consistent, and discoverable insights
- Strong familiarity with AI\-augmented analytics tools, conversational analytics interfaces, and modern augmented analytics platforms, with a demonstrated ability to drive adoption of next\-generation analytics capabilities
- Strong understanding of pharmaceutical commercial data ecosystems, including familiarity with key commercial data vendors and partnership models (e.g., IQVIA, Veeva, Komodo, claims, specialty pharmacy, telehealth)
- Proven ability to partner and influence across Enterprise Insights \& Analytics, Marketing, Sales, Market Access, Advanced Analytics, and IT functions in a matrixed organization
- Experience managing external technology vendors and partners, ensuring value delivery and alignment with enterprise standards
- Excellent communication and stakeholder management skills, with the ability to translate complex AI and analytics concepts into clear, business\-relevant narratives for diverse audiences
- Strong strategic thinking and problem\-solving skills, with the ability to operate effectively in ambiguity and drive outcomes in a complex, fast\-paced organizational environment
The base compensation range for this position is $152,500 to $228,300\. Base compensation is determined based on a number of factors. This position is also eligible for a company bonus based on individual and company performance. Novo Nordisk offers long\-term incentive compensation and or company vehicles depending on the position's level or other company factors.
Employees are also eligible to participate in Company employee benefit programs including medical, dental and vision coverage; life insurance; disability insurance; 401(k) savings plan; flexible spending accounts; employee assistance program; tuition reimbursement program; and voluntary benefits such as group legal, critical illness, identity theft protection, pet insurance and auto/home insurance. The Company also offers time off pursuant to its sick time policy, flex\-able vacation policy, and parental leave policy.
We commit to an inclusive recruitment process and equality of opportunity for all our job applicants.
At Novo Nordisk, we're not chasing quick fixes – we're creating lasting change for long\-term health. For over 100 years, we've been driven by a single purpose: to defeat serious chronic diseases and help millions of people live healthier lives. This dedication fuels our constant curiosity and inspires us to push the boundaries of what's possible in healthcare. We embrace diverse perspectives, seek out bold ideas, and build partnerships rooted in shared purpose. Together, we're making healthcare more accessible, treating and defeating diseases, and pioneering solutions that create change spanning generations. When you join us, you become part of something bigger – a legacy of impact that reaches far beyond today.
Novo Nordisk is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, gender identity, sexual orientation, national origin, disability, protected veteran status or any other characteristic protected by local, state or federal laws, rules or regulations.
If you are interested in applying to Novo Nordisk and need special assistance or an accommodation to apply, please call us at 1\-855\-411\-5290\. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.
Salary Context
This $152K-$228K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Novo Nordisk, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. Disclosed range: $152K to $228K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Novo Nordisk, Inc. AI Hiring
Novo Nordisk, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Plainsboro, NJ, US. Compensation range: $155K - $228K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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