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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.
Global Director Market Data \& AI
Global Director Market Data \& AI is a strategic leadership role with a dual mandate: globally, shaping market data as a critical enterprise asset, and locally, translating data and AI strategy into scalable business impact across multiple priority markets, with the U.S. and Canada as core markets.
This role is central to building a more predictive, connected, and insight\-led commercial engine. The successful candidate will turn trusted market data into competitive advantage by enabling analytics, AI, and decision\-making at global scale while ensuring business teams can act on harmonized, high\-quality, and activation\-ready data.
By combining global domain ownership with multi\-market execution, this role bridges strategy and delivery. It ensures that enterprise standards are shaped by real market needs, while local initiatives benefit from scalable governance, reusable data products, and cross\-market best practices.
As AI becomes a core operating layer for commercial decision\-making, market data is evolving from reporting support into continuous commercial intelligence. This role will help shape that future by strengthening data foundations, improving governance, and enabling intelligent systems that support faster insight generation, better investment decisions, and scalable growth across markets.
YOUR TASKS AND RESPONSIBILITIES:
Global Responsibilities:
- Define the global vision, roadmap, and success measures for market data, including acquisition, governance, activation, and value realization in partnership with Insights \& Analytics, Marketing, Sales, Finance, IT, Procurement, and external data providers.
- Define and enforce global governance standards, taxonomies, data policies, and quality frameworks across the full data lifecycle — from ingestion to consumption — ensuring compliance with GDPR, data privacy, licensing terms, and applicable regulatory requirements in partnership with Legal and IT.
- Own the global market data quality and enrichment strategy, including standards, matching algorithms, and scoring models that keep profiles accurate, deduplicated, complete, and fit for activation and AI use.
- Partner with cross\-functional teams—including Digital, Data, Technology, Customer \& Commercial, Media, and Operating Units—to design global\-to\-local data flows, integration models, and activation strategies that connect structured and unstructured data across markets.
- Establish and coordinate a global network of Data Owners and Business Data Stewards, providing clear accountability, escalation paths, and decision\-making forums across markets.
- Co\-shape the Master Data Strategy, ensuring Market Data aligns with Product, Customer, and Consumer master domains — harmonizing market and channel definitions across providers and systems to enable downstream analytics and AI use cases.
- Ensure Market Data is AI\-ready by design — with robust quality, consistent structures, documented lineage, compliant usage rights, and clear semantic standards — so data can reliably fuel analytics, automation, and intelligent action in collaboration with I\&A Home, IT, Legal, and others.
Multi\-Market Responsibilities:
- Define and execute a multi\-market data and AI roadmap aligned with commercial priorities, with the U.S. and Canada as core markets and a focus on scalable, reusable data products, AI\-enabled workflows, and measurable business outcomes.
- Translate global data domain strategies into effective market execution, capturing feedback from priority markets to continuously strengthen enterprise\-wide strategy.
- Identify, prioritize, and lead data and AI initiatives that deliver measurable business value, including revenue growth, cost optimization, stronger consumer engagement, and faster commercial decision\-making.
- Work backwards from priority market outcomes to redesign data\-enabled and AI\-supported processes, ensuring that information flows, data connections, and decision points are practical for markets to adopt and scalable across product teams.
- Integrate process design, information architecture, data architecture, and user experience considerations to create intelligent, user\-friendly workflows that can be deployed and continuously improved across priority markets.
- Oversee governance, management, and quality for procured and internal data assets, partnering with IT, Legal, Privacy, and business stakeholders to ensure secure, compliant, and value\-driven data usage.
- Collaborate with global and market teams across Marketing, Sales, Supply Chain, Regulatory, Digital, Finance, and Commercial Excellence to understand business needs and translate them into actionable data capabilities.
- Partner with IT to enable scalable, secure, and accessible data platforms that connect business requirements with technical delivery, support cross\-market adoption, and enable responsive information flows from data source to business action.
- Lead cross\-functional working teams and human\-centered change management, creating a clear vision for how AI will augment work, reduce manual effort, and help teams adopt new ways of working with confidence.
WHO YOU ARE:
Bayer seeks an incumbent who possesses the following:
Required Qualifications:
- Master’s degree in business, Economics, Data Science, Statistics, or a related field; bachelor’s degree with equivalent experience also considered.
- Proven experience as a Business Data Domain Owner with end\-to\-end accountability for domain strategy, governance, data quality, priorities, and business alignment.
- Experience managing market data vendors, budgets, contract negotiations, provider consolidation, and license optimization.
- Experience defining and maintaining commercial data dictionaries, metadata, lineage, data caveats, and usage standards.
- Strong knowledge of data governance frameworks, data quality controls, and their practical application in large commercial organizations.
- Track record of translating business outcomes into analytics, automation, and AI use cases enabled by high\-quality, well\-structured, and compliant market data.
- Practical understanding of responsible AI principles, data privacy, third\-party data usage rights, and governance requirements for compliant AI deployment.
- Experience partnering with IT, product, analytics, and business teams to integrate market data into enterprise platforms such as Snowflake, Azure, or Databricks, moving data and AI use cases from concept to scalable adoption.
- Demonstrated success leading data and AI initiatives from strategy to deployment and adoption, including value tracking, user enablement, and measurable impact on business outcomes.
- Experience with AI\-enabled process redesign, including working backwards from business outcomes, defining data and workflow requirements, and supporting adoption across markets or business units.
- Ability to shape structured and unstructured data requirements, semantic definitions, metadata, and interpretation rules needed for analytics, automation, and AI\-powered decision support.
- Strong knowledge of GDPR, third\-party data usage rights, and license management in a regulated environment.
- Strong leadership, communication, and stakeholder management skills.
- Strong ability to lead human\-centered transformation, helping teams understand how AI will augment work, reduce manual effort, and shift contribution toward higher\-value activities.
- Fluent English required; additional languages (Spanish, Polish, German) are an asset.
Preferred Qualifications:
- 10\+ years of experience in market data, commercial analytics, data governance, or data strategy within a complex international FMCG, consumer health, life sciences, or similarly regulated environment.
This posting will be available for application until at least July 21, 2026\.
Employees can expect to be paid a salary between $173280\- 259920\. 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 \|\| United States : Residence Based : Residence Based
Division: Consumer Health
Reference Code: 873219
Contact Us
Email: hrop\_usa@bayer.com
Salary Context
This $173K-$259K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bayer, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $173K to $259K.
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.
Bayer AI Hiring
Bayer has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Whippany, NJ, US. Compensation range: $216K - $259K.
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 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 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).
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 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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