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About This Role
Data Scientist II \- Commercial Insights \& Analytics
Facility: Data \& AI
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
As a member of the Enterprise Data \& Advanced Analytics team within Finance \& Operations, collaborates with team members and other F\&O and IT departments to deliver advanced data and analytics solutions to NNI Commercial Stakeholders, to the benefit of patients, prescribers and other stakeholders. Works across the F\&O organization, with stakeholders across the broader NNI organization and applicable consultants and partners, to lead the design, development and deployment of advanced data and analytics solutions to critical business questions. Applies statistical and machine learning methods – including those powering Next Best Engagement (NBE) and omni\-channel orchestration – to deliver solutions leveraging internal and syndicated commercial datasets such as Xponent Plantrak, LAAD, Experian, Veeva, ICM and others. Partners with Novo Nordisk commercial functions to help measure and optimize promotional investments, inclusive of marketing mix, test \& control, NBE suggestion performance, and promotional targeting initiatives. Provides technical leadership and mentorship to more junior data scientists across the team.
Relationships
Member of the Advanced Analytics team within the Enterprise Data \& Advanced Analytics (ED\&AA) department, in Finance \& Operations, reporting to a Director\-level manager (depending on specific assignment).
Internal relationships include directors and members of other F\&O teams, Commercial Operations, IT, Marketing brand leads, extended brand teams and other Marketing stakeholders, Omni\-channel \& Enterprise Insights, and the Next Best Engagement (NBE) cross\-functional team (Brand, Field, Omnichannel Orchestration, MLOps).
External relationships include secondary data vendors, orchestration platform partners (e.g., Veeva, Salesforce/Marketing Cloud) and consultants. Senior\-level (VP and higher) exposure through presentations on stakeholder targeting, NBE model performance, impact analysis and other related topics.
Essential Functions
- Leads, with ED\&AA team members, F\&O colleagues, and/or LoB stakeholders, the definition of advanced analytics / ML opportunities, determining appropriate datasets and algorithms, identifying patterns and formulating solutions, experimenting and delivering production\-grade analytics and models.
- Owns end\-to\-end delivery of Next Best Engagement (NBE) data science work\-streams – including suggestion/recommendation models, uplift and propensity models, channel sequencing, and HCP\-level decisioning – partnering with IT/MLOps to operationalize models in orchestration platforms (e.g., Veeva CRM Suggestions, Salesforce Marketing Cloud).
- Designs and executes causal measurement frameworks (test vs. control, A/B testing, holdouts, quasi\-experiments) to quantify the incremental impact of NBE suggestions, marketing mix investments, HCP targeting and promotional programs; presents results to senior leadership.
- Collaborates with internal and/or external resources (i.e., consultants and partners) to define and deliver innovative data and analytics solutions in response to business requirements and priorities. Areas of focus include promotional impact analytics, patient analytics, HCP targeting, PLTV, HCP analytics, NBE suggestion performance, and related topics in support of our commercial and enabling functions.
- In collaboration with ED\&AA colleagues, IT, and external resources, supports the development of infrastructure for promotional impact assessment and NBE model lifecycle management – including more predictive/efficient modelling techniques, reusable data pipelines, reusable code repositories, model monitoring/retraining workflows, and dashboard/PPT\-based reporting infrastructure.
- Works with other ED\&AA team members, IT, and data engineers to acquire and transform datasets (e.g., NPP, LAAD, Veeva, ICM, Experian) that deliver critical business insights. Defines and creates metrics that can be leveraged by ED\&AA and Enterprise Insights analysts to deliver insights to their stakeholders.
- Applies advanced data science and statistical analysis in support of organization\-wide projects and programs; explores and evaluates new data\-driven approaches, including emerging GenAI / LLM techniques where relevant.
- Provides technical guidance, code review and QC oversight to more junior team members (onshore and offshore) and to colleagues in other ED\&AA areas in the use and delivery of analytics and analytical tools; mentors others on NBE best practices and responsible AI.
- Communicates findings and recommendations to non\-technical stakeholders via clear visualizations, written narratives and presentations, with a strong ability to translate highly technical work into simple, engaging stories that drive action at senior\-leadership levels.
Physical Requirements
Up to 10% overnight travel required.
Qualifications
- Bachelor 's degree in Math, Computer Science, Statistics, or related field required; MS or MBA preferred
- A minimum of five (5\) years relevant experience in data science, ROI analytics, business analysis, sales analysis, forecasting, quantitative finance, or other related areas, with prior experience in the pharmaceutical manufacturing or closely related industry strongly preferred. Appropriate educational background may be substituted for some experience.
- Demonstrated experience designing, deploying and maintaining production machine\-learning solutions – ideally Next Best Action / Next Best Engagement, recommender systems, uplift / propensity, or HCP targeting / segmentation / persistency models – in an orchestration or omni\-channel context.
- Advanced statistical and programming experience required, with demonstrated application in HCP targeting, HCP segmentation, persistency / retention modeling, and causal / experimental measurement (test \& control, A/B, holdout design).
- Proficient in two or more programming tools such as Python, R, SQL, SAS, etc., and demonstrated ability to learn new analytical and programming techniques required.
- Experience with IQVIA datasets and other industry sources preferred (e.g., Xponent Plantrak, LAAD, Experian, Veeva, ICM); demonstrated experience analyzing and managing large structured/unstructured datasets to generate insights is essential.
- Experience with leading data and visualization platforms highly desirable – e.g., Dataiku, Snowflake, Tableau, Qlik Sense, Plotly Dash, ggplot, etc.
- Experience with version control systems (e.g., Git, GitHub), including collaborative development, code versioning, and reproducible analytics workflows; experience with CI/CD pipelines and MLOps automation frameworks preferred.
- Strong written and verbal communications skills. Demonstrated experience presenting complex analytics and NBE model results to senior\-level audiences and translating them into business action.
- Ability to collect and synthesize complex information, making it relevant, understandable and actionable for key stakeholders. Able to tell stories around data and analytics that resonate with key stakeholders.
- Ability to solve complex, unstructured problems with minimal supervision and to lead workstreams across cross\-functional partners.
- Advanced Excel and PowerPoint skills required.
The base compensation range for this position is $99,600 to $155,400\. 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 $99K-$155K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Novo Nordisk, Inc., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($127K) sits 36% below the category median. Disclosed range: $99K to $155K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Novo Nordisk, Inc. AI Hiring
Novo Nordisk, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in Plainsboro, NJ, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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