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About This Role
At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.
Principal Data Scientist
PURPOSE:
A Principal Data Scientist has complete oversight of data science within a platform. Individuals in this role will:
- Set direction
- Build capability
- Oversee resourcing, budgeting, professionalism and outputs and products
- Support and enable future IT developments
- Understand and use a wide range of data science techniques, tools and technologies
- Lead on ethics
- Communicate and present data science and data ethics effectively to senior leaders
- Champion the role of data science in supporting organizational priorities, and in collaborative working across platforms and functions
- Represent the platform on data science matters
This role will be located in Whippany, NJ. Candidates must live within reasonable commute to the Whippany, NJ site as remote working arrangements will not be accommodated.
YOUR TASKS AND RESPONSIBILITIES: Applied Math, Statistics, and Scientific Practices
- Identify opportunities to develop statistical insight, reports and models to support organizational objectives, while collaborating across the organization effectively;
- Critique statistical analyses;
- Use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle;
- Use model outputs to produce evidence and help design services and policies;
- Understand a broad range of statistical tools, particularly those deployed within the organization, and can use these appropriately and help others to use them;
Data Engineering and Manipulation
- Work with data engineers and data scientists to design and deliver products into the organization effectively;
- Understand the reasons for cleansing and preparing data before including it in data science products and can put reusable processes and checks in place;
- Access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organization;
Data Science Innovation
- Demonstrate practical knowledge of data science tools and techniques;
- Develop data science solutions that maximize insight;
- Identify opportunities for how data science can improve data practices;
Knowledge Graphs, Ontologies, and Generative AI
- Demonstrate strong knowledge of knowledge graph and ontology concepts, including semantic modeling, graph-based data integration, reasoning, and metadata management;
- Apply semantic web standards and technologies, including RDF, OWL, and SHACL, to design, implement, validate, and govern enterprise ontologies and knowledge graphs;
- Use Generative AI and machine learning algorithms to accelerate ontology and knowledge graph development, including concept and relationship extraction, entity linking, taxonomy and schema generation, mapping, enrichment, and quality validation;
- Design human-in-the-loop processes to review, evaluate, and improve GenAI-generated semantic assets for accuracy, traceability, consistency, and business relevance;
- Collaborate with domain experts, data architects, data engineers, and AI engineers to integrate knowledge graphs and ontologies into scalable data and AI products;
Delivering Business Impact
- Champion the role of data science within the organization;
- Understand and champion user research, and can design and manage processes to gather and establish user needs;
- Identify and create opportunities to develop and deliver data science products to support organizational objectives, while collaborating across the organization to fulfil meaningful goals;
- Take responsibility for delivering scalable data science products into the organization, and establishing maintenance support;
Developing Data Science Capability
- Act as a leader or technical specialist, providing detailed support and guidance within the organization and helping colleagues to develop skills
- Set the direction of continuous development plans within the team;
- Keep up to date with new developments in data science and can match those to opportunities in your organization;
- Talk confidently about the benefits of data science approaches to existing and potential customers;
- Demonstrate an in-depth understanding of a wide range of data science techniques, such as machine learning and natural language processing, and detailed knowledge of at least one specialty;
- Use these techniques to build data science solutions, including reports, models and dashboards;
Ethics and Privacy
- Oversee compliance with data ethics standards and legislation;
- Develop and manage the ethical framework for how data, machine learning and artificial intelligence techniques are used across the organization, ensuring data governance complies with relevant legislation and standards;
- Embed a culture of data ethics and explain why this is so important;
- Ensure ethics guidance is appropriately applied to the formulation, implementation and evaluation of policies and programs;
- Assess and constructively challenge proposed policies and programs;
Programming and Build
- Write and test scripts and create basic models in one or more languages
- Collaborate on shared codebases, using a variety of methodologies;
- Understanding Product Delivery;
- Understand the differences between delivery methods, such as Agile and Waterfall, and can choose the most appropriate method to deliver each product;
- Define the minimum viable product (MVP) and support decisions about priorities;
- Work with specialists in multidisciplinary teams to smoothly deliver data science products into the organization;
Working Relations
- Product Managers
- UX Designers
- Data Scientists
- Data Architects
- Data Engineers
- Data Stewards
- Enterprise Architects
- Software Engineers
- Relevant business leaders and stakeholders
WHO YOU ARE:
Bayer seeks an incumbent who possesses the following:
Required Qualifications:
- Minimum of a Master’s Degree in Computer Science, Statistics, Mathematics, Data Science, or a related field, or a Bachelor’s Degree with minimum 10 years of related experience;
- Strong academic background with coursework or research in machine learning, AI, statistical modeling, and data analysis;
- Proficiency in programming languages such as Python or R for data analysis and machine learning;
- Familiarity with libraries and frameworks like TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy;
- Advanced knowledge of statistical methods and techniques;
- Experience in hypothesis testing, regression analysis, clustering, and classification;
- Expertise in machine learning algorithms and techniques;
- Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark);
- Proficiency in SQL, relational database, and NoSQL databases;
- Hands-on experience designing or implementing knowledge graphs and ontologies using RDF, OWL, and SHACL, including experience applying Generative AI techniques to support their creation, enrichment, mapping, or validation.
Preferred Qualifications:
- Strong analytical and problem-solving skills;
- Ability to evaluate assumptions and limitations of data;
- Excellent verbal and written communication skills;
- Experience working collaboratively in interdisciplinary teams;
- Demonstrated experience in leading and mentoring other data scientists;
- Ability to align data science initiatives with business objectives;
- Strong decision-making skills.
This posting will be available for application until at least 10/19/2026.
Employees can expect to be paid a salary between $151,120 - $226,680. Additional compensation may include a bonus or commission (if relevant). Other benefits include health care, vision, dental, retirement, PTO, sick leave, etc. If selected for this role, the offer may vary based on market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.
YOUR APPLICATION
Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
Bayer is an Equal Opportunity Employer/Disabled/Veterans
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.
Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders.
Bayer is an E-Verify Employer.
Location: United States : New Jersey : Whippany
Division: Enabling Functions
Reference Code: 884422
Contact Us
Email: [email protected]
Salary Context
This $151K-$226K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Bayer, 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 $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $151K to $226K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Bayer AI Hiring
Bayer has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Whippany, NJ, US, Berlin, Berlin, Germany, Washington, DC. Compensation range: $226K - $226K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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