Head of Machine Learning Research

We need to output HTML fragment with sections: About the role? The posting includes introductory paragraph about Bayer vision etc. That could be "About the role" or maybe "About Bayer". The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use

• for any list of duties/requirements/benefits, and

  for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

  or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. Output ONLY the HTML fragment — no /, no markdown code fences. We need to decide which sections to include. The posting includes: - Intro paragraph about Bayer vision and mission (could be "About the role" or maybe "About Bayer"). - Then "Head of Machine Learning Research" title (we shouldn't repeat as heading). Then description: "We are seeking a scientific and technical leader to advance machine learning–enabled molecular design across our drug discovery portfolio." etc. That's part of role description. - Then "Your Tasks And Responsibilities" with subheadings: Strategy and Technical Direction, Delivery into Discovery Projects, Platform and Data Foundation, Leadership and Organization. Those are duties. - Then "Who You Are" with Required Qualifications and Preferred Qualifications. - Then salary range: "Employees can expect to be paid a salary between $177,400.00 - $266,200.00." Additional compensation may include a bonus or commission (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc. This salary range is merely an estimate and may vary based on... This is Pay and Benefits info. - Then "This posting will be available for application until at least 09/29/2026." That's maybe schedule? Not exactly schedule but posting deadline. Could be considered Schedule? Probably not; schedule refers to work schedule. Might omit. - Then "YOUR APPLICATION" etc: promotional fluff, maybe drop as application instructions. - Then boilerplate about recruitment agencies, EEO, etc: remove. Thus we need sections: About the role (intro + role description), Responsibilities (the tasks), Qualifications (required + preferred), Benefits (health care etc), Pay (salary range). Possibly also "Schedule"? Not present. We must not create sections with filler. Ensure we include all substantive info. We need to use

  for each section heading. Use

  • for any list of duties/requirements/benefits. Use

    for prose. We need to preserve all substantive info, not invent or summarize away detail. So we need to keep details like specific ML methods, team size, etc. We need to format responsibilities as lists. The responsibilities have subheadings; we could either make each subheading as a heading? But instruction says use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid using headings inside sections? It says use

    for section headings (list). Likely they want only those top-level headings. Inside sections we can use

    and

    • . So we should not create additional headings like "Strategy and Technical Direction". Instead we can incorporate those as list items under Responsibilities. Thus we need to extract duties from the responsibilities section and put them as list items under a
      inside Responsibilities

      . Similarly, qualifications: required and preferred can be list items under Qualifications

      . Benefits list under Benefits

      . Pay under Pay

      maybe as

      with salary range. We must not repeat job title as heading. So no

      for Head of Machine Learning Research. We need to include location? Possibly as minor metadata folded into a short

      or drop it. The location appears at end: "Location: United States : Massachusetts : Cambridge || United States : New Jersey : Whippany Division: Pharmaceuticals Reference Code: 885870". That's minor metadata; we can fold into a short

      maybe under About the role or at end. But instruction: do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

      or drop it. So we can include a

      with location and reference code. We must remove boilerplate like EEO, etc. Now craft HTML. Structure:

      About the 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.

      We are seeking a scientific and technical leader to advance machine learning–enabled molecular design across our drug discovery portfolio. As a member of the Data Science & AI leadership team, you will define and deliver the models, platforms, and ways of working that shorten design–make–test–analyze cycles and improve the quality of molecules progressing toward nomination. This is a hands-on strategic leadership role—neither research-only nor delivery-only. You will set a multi-year technical roadmap, build and lead a team of approximately 25 scientists and engineers, and ensure that machine learning capabilities translate into better project decisions and faster progression to candidate selection. You will lead the development and application of modern machine learning methods—including deep learning, neural networks, ensemble methods, natural language processing, and machine perception—to complex, high-dimensional scientific data. Your team will develop predictive and generative models that help discovery scientists understand biological and chemical patterns, prioritize experiments, optimize molecular properties, and design novel molecules. Success in this role requires deep fluency in contemporary machine learning, combined with a practical understanding of the realities of medicinal chemistry, structural biology, and drug discovery. You will spend as much time engaging in project-team reviews and scientific decision-making as you do shaping model architectures, platform strategy, and technical standards.

      Then maybe location meta:

      Location: United States – Massachusetts – Cambridge or United States – New Jersey – Whippany. Division: Pharmaceuticals. Reference Code: 885870.

      Then Responsibilities:

      Responsibilities

      • Strategy and Technical Direction: Co-own the vision, roadmap, and investment case for ML-driven molecule design and multi-parameter optimization across small molecules and emerging modalities. Make build/buy/partner decisions across generative design, property prediction, structure-based ML, and synthesis planning; evaluate foundation models and external platforms with rigor rather than hype. Set the standard for how models are validated, benchmarked prospectively, and retired — including uncertainty quantification, applicability domain, and honest reporting of failure modes.
      • Delivery into Discovery Projects: Partner with Drug Discovery Sciences — medicinal chemistry, structural biology, biophysics, screening, DMPK, and safety — to embed ML into live programs from hit identification through candidate nomination. Co-own program-level design goals with chemistry leads: potency, selectivity, ADMET, developability, and IP position optimized together rather than sequentially. Drive active learning and closed-loop DMTA cycles, including integration with high-throughput and automated synthesis where available. Ensure models are delivered as reliable, supported products that chemists actually use, not one-off analyses.
      • Platform and Data Foundation: Direct development of core capabilities: ADMET and property predictors, generative and de novo design, retrosynthesis and synthetic accessibility, and structure-based ML including co-folding, pose prediction, docking rescoring, and ML-accelerated free energy methods. Work with data engineering to secure the assay, structural, and DMTA data foundation these models depend on — curation, provenance, harmonization across assays and sites, and feedback capture from every make-test cycle. Establish MLOps practice appropriate to a regulated R&D environment: versioning, reproducibility, monitoring, and documentation.
      • Leadership and Organization: Build, lead, and develop a team of approximately 25 spanning ML research, computational chemistry, and ML engineering; grow leaders within the group. Own budget, vendor relationships, and external collaborations with academic groups, consortia, and biotech partners. Represent the function to R&D leadership, translating technical capability into portfolio impact and translating portfolio priorities into technical strategy. Contribute to the broader scientific community through publication, presentation, and precompetitive collaboration where appropriate.
      Then Qualifications:

      Qualifications

      Required Qualifications:

      • PhD in computational chemistry, cheminformatics, structural biology, biophysics, computer science, or a related field, with demonstrated depth in both a molecular science and machine learning. Exceptional candidates with an MSc and equivalent depth of experience will be considered.
      • Deep, current expertise across modern ML for molecules: graph neural networks, transformers, generative approaches (diffusion, flow matching, autoregressive), Bayesian optimization and active learning, transfer learning on sparse assay data, and uncertainty quantification.
      • Working command of the discovery domain: SAR interpretation, multi-parameter optimization, ADMET and developability, protein structure and ligand binding, docking and free energy methods.
      • Demonstrated impact on real programs — molecules advanced, cycles shortened, decisions changed. A publication record without program impact is not sufficient for this role.
      • Proven ability to partner with medicinal chemists and structural biologists as scientific peers, including the credibility to challenge and be challenged on design decisions.
      • Track record of delivering software or models into production use by scientists, not just prototypes.

      Preferred Qualifications:

      • 12+ years of relevant experience, including 5+ years leading technical teams; experience leading leaders strongly preferred.
      • Experience with modalities beyond conventional small molecules — PROTACs and molecular glues, macrocycles, peptides, covalent inhibitors, or oligonucleotides.
      • Familiarity with lab automation, self-driving lab concepts, or high-throughput chemistry integration.
      • Experience evaluating or deploying large-scale pretrained models for chemistry or protein structure.
      • Prior experience in a large matrixed pharma R&D organization, or scaling a capability from startup to enterprise.
      • Strong external profile: publications, conference presence, or leadership in precompetitive consortia.
      Then Benefits:

      Benefits

      • Health care
      • Vision
      • Dental
      • Retirement
      • PTO
      • Sick leave
      Then Pay:

      Pay

      Employees can expect to be paid a salary between $177,400.00 - $266,200.00. Additional compensation may include a bonus or commission (if relevant). This salary range is merely an estimate and may vary based on an applicant’s location, market data/ranges,