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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 Consolidated Customer Data \& AI Lead
The Global Consolidated Customer Data \& AI Lead is a strategic leadership role with a dual mandate: globally, shaping consolidated customer data as a critical enterprise asset, and locally, translating Data \& 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 connected, trusted, and insight\-led commercial engine. The successful candidate will turn harmonized customer data from Retailer \& Omni \- POS, trade systems, master data platforms, Category Management \& Space Planning, and third\-party providers into competitive advantage by enabling analytics, AI, omnichannel engagement, customer segmentation, and decision\-making at global scale, while ensuring business teams can act on 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, data products, and governance frameworks are shaped by real market needs, while initiatives markets i.e. U.S. and Canada benefit from scalable governance, reusable customer data products, and cross\-market best practices.
As AI becomes a core operating layer for commercial decision\-making, customer data is evolving from fragmented records and reporting support into continuous commercial intelligence. This role will help shape that future by strengthening data foundations, improving governance, resolving duplication, and enabling intelligent systems that support faster insight generation, more personalized engagement, better investment decisions, and scalable growth across markets, channels, and customer types.
YOUR TASKS AND RESPONSIBILITIES:
Global:
- Define the global vision, roadmap, and success measures for consolidated customer 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 consolidated customer 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 Customer Data aligns with Product, Market, and Consumer master domains — harmonizing market and channel definitions across providers and systems to enable downstream analytics and AI use cases.
- Ensure Customer 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:
- Co\-Shaping in defining and executing the multiple markets with focus on US and Canada Data \& AI strategy and a focus on scalable, reusable data products, AI\-enabled workflows, and measurable business outcomes.
- Translate global data domain strategies into effective execution in Focus markets US and Canada and provide market feedback to strengthen enterprise\-wide direction.
- Support NA data governance, management, and quality for customer and procured data assets, partnering with IT to implement frameworks that ensure quality, security, and compliance with US and Canadian regulations.
- Collaborate with cross\-functional stakeholders across Sales, Marketing, Medical, Digital Commerce, Supply Chain, Regulatory, and Customer Experience to translate data needs into actionable insights and data products.
- Work closely with IT to enable scalable, secure, and accessible data platforms that align technical capabilities with Focus markets US and Canada business needs and the multi\-market data roadmap.
- 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.
- Support cross\-functional teams in Focus markets US and Canada by promoting a data\-as\-an\-asset mindset, AI adoption, innovation, and continuous improvement, while serving as a senior data partner to commercial and functional leaders.
- Manage day\-to\-day Focus markets US and Canada data priorities w, including vendor relationships, issue escalation, stakeholder requests, and reporting on data health and initiative progress.
WHO YOU ARE:
Bayer seeks an incumbent who possesses the following:
Required Qualifications:
- Master’s degree in Business, Economics, Data Science, Information Management, or a related field, or a bachelor’s degree with equivalent experience.
- Proven experience as a Business Data Domain Owner with end\-to\-end accountability for domain strategy, governance, quality, priorities, and business alignment, ideally in customer or account data.
- Deep knowledge of customer data ecosystems, master data, retailer hierarchies, distributor records, identity resolution, matching and deduplication, and third\-party enrichment sources.
- Strong knowledge of data governance frameworks, master data management principles, and golden record logic, with the ability to apply them in large commercial organizations operating across channels and markets.
- Track record of enabling analytics and AI use cases through high\-quality, well\-structured, and compliant customer data, with an understanding of responsible AI principles.
- AI\-native mindset with strong curiosity, hands\-on AI literacy and tool use, and the ability to identify, experiment with, and scale practical AI\-enabled ways of working across data, analytics, and business processes.
- Experience partnering with IT to integrate customer data into enterprise platforms such as Snowflake, Azure, Databricks, Veeva, or MDM systems, including defining business requirements, validating delivery, and driving adoption.
- Demonstrated success leading customer data consolidation or harmonization initiatives that deliver measurable business value, such as a single customer view, account deduplication, or cross\-system integration.
- Proven experience working with IT teams on data strategy, governance, architecture, and compliance.
- Strong knowledge of GDPR, US state privacy laws such as CCPA, third\-party data usage rights, and license management in regulated environments.
- Experience with the Focus markets US and Canada commercial data landscape, including US healthcare data considerations, retail and pharmacy account structures, and multi\-market data provider ecosystems.
- Strong leadership, communication, and stakeholder management skills, with the ability to influence without direct authority in a global matrix environment.
- Fluent English required; additional languages such as French or Spanish are an advantage.
Preferred Qualifications:
- 7\+ years of experience in customer data management, data governance, master data strategy, or commercial data leadership in a complex international FMCG, Consumer Health, Pharmaceuticals, 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 $144160\- 216240\. 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: 873220
Contact Us
Email: hrop\_usa@bayer.com
Salary Context
This $144K-$216K 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $144K to $216K.
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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