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About This Role
Senior Data Scientist
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 Senior Data Scientist will help drive the NNI Advanced Analytics vision to explore and develop new solutions that may hold the potential to add value to patients and Novo Nordisk in the future within therapeutic areas covered by Novo Nordisk corporate and commercial strategies. The Senior Data Scientist will build machine learning\-based tools and processes within the company’s current big data infrastructure such as recommendation engines, automated propensity scoring systems, and A/B testing procedures. The Senior Data Scientist will work with Novo Nordisks Big Data Analytics COE and other similar roles in Medical Information and Analytics, Medical Data Analytics, and HR People Research and Analytics to help foster and grow a community of predictive and prescriptive analytics.
Relationships
The Senior Data Scientist reports to the Director of Data Science. Internal relationships include other Commercial Effectiveness functions, especially Analytics and Data Governance; Big Data Analytics COE, Commercial Product and Portfolio personnel, and Area Commercial Leads. Extended internal relationships could include those in other Data Science/Data Science\-like roles in Clinical, Medical, Regulatory, Human Resources, Global Development and Seattles Device Research center. External relationships include relationships with commercial collaboration partners as well as academia, where necessary.
Essential Functions
- The Senior Data Scientist is expected to:
+ Develop Machine Learning\-based solutions using available commercial data, including but not limited to, IQVIA (IMS) data, Real World Data (e.g., Truven), Financial Data, Sales Force Automation (e.g., Veeva) data, and integrated campaign management (e.g., Adobe) data
+ Build and maintain processes to acquire, process and curate necessary data for analysis and insights
+ Mine and analyze data to identify insights pertaining to dynamic and/or micro segmentation of patient, physician, and payer/delivery providers
+ Translate analytic insights into real world solutions
+ Design methods to generate real\-time predictive and descriptive analytics from big datasets
+ Take responsibility in making new, relevant solutions to real Commercial problems and be able to show successful implementation
+ Occasionally participate in network with external collaboration partners, e.g., academia, other Pharmaceutical and Non\-pharmaceutical Data Science departments specializing in improving commercial results through data science techniques
Physical Requirements
0\-10% overnight travel required.
Qualifications
- Education: A minimum of a Bachelor's Degree within relevant field (e.g., Computer Science, Applied Mathematics, Physics, Engineering)
+ Advanced degree may be substituted for experience as appropriate
- 8 years of experience from relevant industry such as pharmaceutical, biotechnology, consumer product, or medical device industries.
- Required knowledge related to machine learning, including supervised, unsupervised, reinforced (deep) learning methods and ensemble learning methods.
+ Specifically, algorithms such as k\-NN, Linear and Generalized Linear Models, naml, Bayes, SVM, and Random Forest
- Preferred knowledge related to how pharmaceutical companies market and sell products, market forces that impact decisions made to improve commercial outcomes, and how machine learning models can improve such outcomes
- Excellent written and oral communication skills required
- Proven ability to influence, communicate, and collaborate across the local organization
- Broad, detailed understanding and mastery of technical area with a track record of analyzing critical data through the application and optimization of distinct analytical skills
- Record of translating technical mastery to significant project impact
- Ability to work independently as well as working in teams. Included in this is the ability to network with external parties
- Demonstrated ability to generate valuable and relevant ideas, create concepts based on ideas, and develop new solutions based on concepts
- Deep knowledge of at least one relevant technical area: computer science, computational statistics, including Bayesian inference, mathematics, or systems dynamics
- Required experience within several of the following areas:
- Experience with analytics tools such Python, R, or SAS and SQL, PowerPivot
The base compensation range for this position is $120,300 to $222,600\. 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 $120K-$222K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 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 $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($171K) sits 11% below the category median. Disclosed range: $120K to $222K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; 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: $222K - $222K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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