{"id":577464,"url":"https://alion.io/job/amgen-senior-data-scientist-protein-structure-ml-models","title":"Senior Data Scientist - Protein Structure ML models","company":{"id":4775,"name":"Amgen","domain":"amgen.com","url":"https://alion.io/company/amgen","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":85,"open_postings":349,"ghost_share":0,"stale_share":0.579,"repost_share":0.003,"time_to_fill_p50_days":42,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Antibody Engineering","optional":true},{"name":"JAX","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-09-09T18:44:53Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-24T16:13:48Z","board_verified":true,"closed_at":null,"days_open":46,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":46},"description":"Career Category\nResearchJob Description\nSenior Data Scientist - Protein Structure ML models\nRole Summary\nThe Senior Data Scientist - Protein Structure ML models will play a critical role in advancing AI-enabled protein and antibody design across Large Molecule Discovery (LMD). This role will focus on building, adapting, and validating machine learning models that predict protein function from structure, with particular emphasis on antibodies and antibody-like molecules.\nWorking at the intersection of machine learning, structural biology, protein engineering, and experimental discovery, this individual will also develop workflows that combine structure prediction, structure generation, and inverse-folding models into practical protein design pipelines. The role will partner closely with wet-lab scientists to guide assay design, generate high-value property data, and translate internal and externally available datasets into rigorous validation strategies.\nThis role is ideal for someone who enjoys developing technically rigorous geometric ML methods while remaining deeply connected to experimental validation and real-world biologics discovery needs.\nKey Responsibilities\nProtein Structure to Function Modeling\nBuild machine learning models, either developed in-house from scratch or adapted from existing examples, to predict protein function from structure.\nFocus model development on antibodies and antibody-like molecules, including formats that require structure-aware modeling approaches for downstream property prediction.\nUse internal and externally available structural, sequence, binding, and property datasets to evaluate and improve model performance.\nAntibody Binding Model Validation\nDesign, own, and maintain validation tasks for assessing antibody binding models across internal and externally available datasets.\nEstablish well-curated benchmarks that support quality control, model comparison, and responsible onboarding of external / open-source ML models.\nTranslate validation results into actionable guidance for model selection, model improvement, and downstream protein design decisions.\nExperimental Data Generation Partnership\nPartner with wet-lab scientists and structural biologists to guide assays for collecting property data that can improve model training, validation, and decision-making.\nHelp define data collection strategies that connect experimental readouts to ML model objectives and biologics design hypotheses.\nWork cross-functionally to interpret experimental outcomes and incorporate learnings into iterative model development workflows.\nIntegrated Protein Design Workflows\nDevelop workflows that chain or efficiently combine structure prediction, structure generation, and inverse-folding models for protein design.\nApply structure-aware ML tools to support de novo design and optimization of antibodies and related formats.\nContribute reusable workflows, validation practices, and technical standards that improve scalability and reproducibility of protein design pipelines.\nBasic Qualifications\nBachelor’s degree in Computational Biology, Bioinformatics, Life Sciences, Computational Chemistry, Chemical Engineering, Materials Science, Data Science, or a related quantitative field and relevant professional experience.\nExperience Requirements\nBachelor’s degree and 6+ years of relevant experience, OR\nMaster’s degree and 4+ years of relevant experience, OR\nPhD\n·\nPreferred Qualifications\nProgramming and ML Frameworks\nFluent in Python and able to develop reliable, reusable scientific software for model development, data analysis, and workflow automation.\nHands-on experience with at least one modern deep learning framework, such as PyTorch or JAX.\nAbility to work across research codebases, adapt existing implementations, and mature promising examples in the literature into robust internal workflows.\nProtein Structure Modeling\nStrong foundation and hands-on experience with protein structure modeling, particularly as applied to antibodies and antibody-like molecules.\nExperience translating structural biology concepts into computational features, modeling assumptions, and validation strategies.\nStructural modeling experience for small molecules is also welcome, especially where those skills are transferable to understanding protein surfaces.\nDeep Learning for Protein Structures\nExperience applying deep learning to protein structures, including graph neural networks, equivariance-aware methods, or related architectures.\nAbility to evaluate structure-conditioned models for protein function, binding, designability, or related protein engineering objectives.\nComfortable working with noisy, sparse, or heterogeneous biological datasets and designing validation strategies that reflect practical discovery constraints.\nGenerative and Inverse Design Tools\nExperienced in applying out-of-the-box generative protein structure tools, such as RFdiffusion or Boltzgen, to protein design questions.\nPreferred experience extending or adding features to open source structure prediction or generative modeling tools.\nPreferred experience using inverse design tools such as ProteinMPNN or related sequence-design methods within end-to-end protein design pipelines.\nScientific Collaboration and Communication\nAbility to collaborate effectively with wet-lab scientists, protein engineers, structural biologists, ML researchers, and data engineering partners.\nStrong communication skills for explaining model assumptions, validation results, and design recommendations to both technical and scientific audiences.\nStrong problem-solving skills, scientific judgment, and attention to detail in the development and deployment of protein design workflows.\nSuccess Measures\nSuccess in this role will be demonstrated through:\nDelivery of validated ML models that predict protein function from structure and support antibody and antibody-like molecule design.\nCreation of robust validation tasks that provide clear quality control for internal and external antibody binding models.\nImproved ability to guide wet-lab data generation toward high-value property measurements for model training and validation.\nEffective integration of structure prediction, structure generation, and inverse-folding tools into reproducible protein design workflows.\nIncreased confidence in model-driven design decisions through well-documented benchmarks and validation practices.\nPositive adoption of protein structure ML workflows by AI, computational biology, and experimental discovery teams.\nTypical Candidate Profile\nThe ideal candidate combines strong machine learning expertise with practical experience in protein structure modeling and biologics discovery. They are comfortable building models, adapting frontier methods, and designing validation tasks that determine whether those models are useful for real discovery decisions.\nCandidates may come from computational biology, machine learning, structural biology, protein engineering, bioinformatics, or related quantitative backgrounds. They are motivated by the opportunity to connect de novo protein design, antibody engineering, experimental validation, and scalable ML workflows into practical discovery capabilities.\nOrganizational Impact\nThis role contributes directly to de novo protein design efforts by developing and validating ML-enabled workflows that connect protein structure to function. By using internal datasets to guide de novo ML models, the Senior Data Scientist - Protein Structure ML models will help enable the design of exotic antibody-like formats and expand the range of biologics concepts that can be explored computationally. The role will also provide critical quality control for onboarding and pipeline use of external ML models through well-curated validation tasks, improving confidence in model-driven design decisions across Large Molecule Discovery.\n.","description_format":"text","description_chars":7884,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Prescription Drugs","Immunology & Inflammation Therapeutics","Cardiometabolic Therapeutics"],"lifecycle":[{"event":"open","at":"2026-09-09T18:44:53Z"}],"visa":[],"liveness":{"score":25,"band":"fade","label":"Fading","p_open":1,"p_active":0.569,"p_room":0.44,"age_days":46,"expected_fill_days":42,"reasons":["conf:37","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-26T05:57:25Z"},"pay":null,"html_url":"https://alion.io/job/amgen-senior-data-scientist-protein-structure-ml-models","json_url":"https://alion.io/job/amgen-senior-data-scientist-protein-structure-ml-models.json","meta":{"generated_at":"2026-10-26T08:03:18Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":10660,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-27T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4775},"rest":"https://alion.io/mcp/rest/get_company?id=4775"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Famgen-senior-data-scientist-protein-structure-ml-models"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Famgen-senior-data-scientist-protein-structure-ml-models"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Famgen-senior-data-scientist-protein-structure-ml-models"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/amgen-senior-data-scientist-protein-structure-ml-models\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famgen-senior-data-scientist-protein-structure-ml-models"}]}