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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.
Director, Decision Intelligence \& AI Product Strategy
PURPOSE:
The Director, Decision Intelligence \& AI Product Strategy is the senior analytical architect and strategic thought partner for the US Asundexian Product Squad. Reporting to the Senior Director who leads Decision Intelligence (DI) for Asundexian, you translate the business’s most important decisions into clear analytical blueprints, design the approach and evidence needed to answer them, and ensure the resulting insights are embedded into business workflows and actions.
In this role, you will help shape strategic priorities alongside the Senior Director while also rolling up your sleeves to conduct sophisticated analyses and generate breakthrough insights. You will connect business context, analytics, research, data and technology so that decision products address the Product’s most important needs and generate measurable business value.
Working across the full decision\-product lifecycle, you will partner closely with other groups within DI to design, build, validate, deploy, operate and continuously improve reusable analytical capabilities. While accountability shifts across the lifecycle, you hold primary accountability for architecting the analysis — framing decisions into blueprints, defining methods and evidence, and assuring analytical quality — and you own the interpretation, adoption and decision impact of the analytics you shape.
This position combines the intellectual rigor of deep analytical work with the strategic impact of advising senior leadership on high\-stakes business decisions. You will work closely with the Senior Director to drive high\-quality insights, effective decisions, product growth and competitive advantage.
The preferred location for this role is Whippany, NJ, however, residence\-based candidates in the US will be considered based upon needs of the business.
YOUR TASKS AND RESPONSIBILITIES:
The primary responsibilities of this role are to:
- Serve as a strategic thought partner and analytical architect for the Product Squad
+ Act as a trusted advisor to Product leadership, and partner with the Senior Director to shape and prioritize the Product’s decision agenda by expected value, urgency and strategic importance.
+ Translate business questions into analytical blueprints that define the required evidence, data, methods, KPIs, drivers and measures of success.
+ Design analytical blueprints that define the decisions, users, cadence, evidence, data, methods, KPIs and measures of success, distinguishing one\-time questions from recurring decisions that warrant a reusable decision product.
+ Generate insights through the synthesis of multiple data sources, and lead major analytical deliverables including situation analyses, forecasting, launch analytics, performance analytics and reporting.
+ Conduct sophisticated analyses and market research such as patient journey mapping, segmentation and competitive intelligence.
- Architect and deliver analytics across an integrated Decision Intelligence operating model
+ Determine whether a business need is a one\-time analytical question or a recurring decision that should be supported through a reusable decision product.
+ Own the analytical blueprint end\-to\-end — establishing business context, success measures, driver architecture, evidence requirements and decision workflows — while partnering with Integrated Decision Products and Data, AI \& Product Engineering, recognizing that primary accountability shifts across lifecycle stages.
+ Validate the analytical approach as products are built, then confirm business fitness, traceability and appropriate release controls before embedding insights into business workflows and enabling users.
+ Monitor quality, performance and usage to recommend what to enhance, scale, recalibrate or retire, and work with partners to resolve recurring failure patterns in models, assumptions, business rules and user experience.
+ Co\-own product design, scope and priority trade\-offs with other groups within DI on data needs, standards and technical requirements.
+ Promote reuse of analytical capabilities and decision products where common business needs and workflows exist.
WHO YOU ARE:
Bayer seeks an incumbent who possesses the following:
Required Qualifications:
- Minimum of a Bachelor’s degree;
- Extensive experience in pharmaceutical or healthcare commercial market intelligence, including primary research, secondary research, third\-party data analytics and vendor management;
- Strong knowledge of the US healthcare environment;
- Proven track record of serving as a strategic advisor to senior business leaders;
- Demonstrated ability to frame complex business decisions and translate them into analytical blueprints, hypotheses, KPIs and actionable recommendations;
- Experience leading analytical initiatives or products from initial problem framing through deployment, adoption and continuous improvement;
- Strong analytical and synthesis skills, including hands\-on experience executing complex analyses and facilitating insight generation;
- Excellent strategic thinking and problem\-solving capabilities;
- Comprehensive knowledge of relevant pharmaceutical data sources, including IQVIA, Komodo, Symphony, laptop and iPad detailing data, Specialty Pharmacy data and formulary data;
- Strong verbal and written communication skills, demonstrated through executive presentations, succinct PowerPoint narratives, structured syntheses and clear recommendations;
Preferred Qualifications:
- Postgraduate degree (such as a Master’s or PhD) in Health Economics, Statistics, Management Science, Business, Marketing or a related field;
- Ten or more years of relevant work experience;
- Deep understanding of health system and IDN dynamics;
- Market access analytics experience.
This posting will be available for application until at least 08/26/2026\.
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.
\#LI\-US
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: Pharmaceuticals
Reference Code: 879410
Contact Us
Email: hrop\[email protected]
Salary Context
This $151K-$226K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($188K) sits 12% below the category median. Disclosed range: $151K to $226K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bayer AI Hiring
Bayer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Whippany, NJ, US. Compensation range: $226K - $226K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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