{"id":577466,"url":"https://alion.io/job/amgen-senior-data-scientist-protein-data-pipelines","title":"Senior Data Scientist - Protein Data Pipelines","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":"B","score":83,"open_postings":336,"ghost_share":0,"stale_share":0.682,"repost_share":0.018,"time_to_fill_p50_days":42,"computed_at":"2026-11-10T06:52:35Z"}},"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":"Databricks","optional":true},{"name":"MLFlow","optional":true},{"name":"Python","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-09-09T18:44:53Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-11-10T05:00:42Z","board_verified":true,"closed_at":null,"days_open":61,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":61},"description":"Career Category\nResearchJob Description\nSenior Data Scientist - Protein Data Pipelines\nRole Summary\nThe Senior Data Scientist - Protein Data Pipelines will play a critical role in enabling predictive modeling for protein sequence, structure, and function by building scalable, reliable, and reproducible data pipelines. This role will focus on transforming protein property data and related scientific outputs into ML-amenable assets that support model training, inference, deployment, and ongoing use across research programs.\nWorking at the intersection of data engineering, MLOps, computational biology, and applied machine learning, this individual will partner with ML developers, wet-lab scientists, domain experts, and distributed technical teams to translate scientific and engineering needs into robust data and inference solutions. The successful candidate will develop reusable frameworks for data engineering, model inference, deployment, validation, testing, and monitoring across in-house and external machine learning models.\nThis role is ideal for someone who enjoys building production-ready scientific data systems, collaborating across disciplines, and converting complex domain needs into maintainable technical solutions that scale across discovery pipelines.\nKey Responsibilities\nScalable Data Pipelines for model training\nDesign and maintain scalable data pipelines that support predictive model training, with emphasis on protein sequence or structure-to-function applications.\nBuild ML-model amenable data assets for protein property data that are readable, quality-controlled, reproducible, and suitable for reuse across programs.\nTranslate scientific and engineering needs into reliable data solutions that support ongoing research and model-development workflows.\nModel Deployment & Inference Pipelines\nDevelop deployment strategies and pipelines to embed trained models into ongoing projects.\nDevelop reusable inference, deployment, and testing frameworks for in-house and external machine learning models.\nConvert model-development outputs into maintainable technical solutions that can be used reliably by research teams.\nData Quality, Validation & Reproducibility\nEstablish data quality, validation, monitoring, and reproducibility practices for protein property and related scientific datasets.\nImplement validation and monitoring approaches that improve confidence in downstream model training, inference, and deployment.\nDocument data lineage, assumptions, validation outcomes, and reproducibility practices to support long-term reuse.\nCross-Functional Collaboration & Technical Coordination\nServe as a liaison between machine-learning developers and domain experts, including wet-lab collaborators where applicable.\nOwn and mediate collaborations between ML developers and wet-lab teams to ensure that data, modeling, and experimental needs are aligned.\nCoordinate technical work across distributed teams and help align implementation plans, dependencies, and delivery timelines.\nDocumentation & Knowledge Sharing\nDocument systems, pipeline behavior, operational expectations, and technical decisions to support adoption and maintenance.\nSupport knowledge sharing across research, ML, and engineering teams through clear documentation, examples, and reusable implementation patterns.\nScale data and modeling infrastructure practices across research programs and 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\nPreferred Qualifications\nScalable Data Engineering\nStrong experience building scalable data pipelines in Python and/or SQL.\nExperience designing readable, reusable, and maintainable data-processing workflows for scientific or machine-learning applications.\nExperience with data pipeline automation, preferably using Databricks.\nMLOps, Inference & Deployment\nHands-on experience owning reusable, end-to-end MLOps for at least one machine learning model.\nExperience with MLflow is preferred; experience with other model-lifecycle, deployment, or tracking frameworks is also welcome.\nExperience developing deployment, inference, validation, or testing workflows that support production-like use of machine learning models.\nData Quality, Monitoring & Reproducibility\nKnowledge of data quality control, validation, and monitoring practices.\nExperience applying reproducibility practices to scientific data, model-training datasets, or inference workflows.\nAbility to identify data quality risks and develop practical controls for downstream model use.\nScientific Domain Experience\nFamiliarity with computational biology, computational chemistry, computational materials science, or related fields.\nExperience working with protein sequence, protein structure, protein property, or related scientific datasets is beneficial.\nPreferred experience collaborating with wet-lab teams and translating experimental needs into data or modeling workflows.\nCommunication & Collaboration\nAbility to communicate effectively with machine-learning developers, software and data engineers, domain experts, and research scientists.\nExperience coordinating technical work across distributed or cross-functional teams.\nStrong documentation habits and commitment to knowledge sharing, maintainability, and long-term adoption.\nSuccess Measures\nSuccess in this role will be demonstrated through:\nDelivery of readable, quality-controlled, and reproducible data pipelines for protein property data.\nSuccessful embedding of trained models into ongoing projects through reliable deployment and inference pipelines.\nIncreased reuse of data-engineering, inference, deployment, validation, and testing frameworks across research programs.\nImproved confidence in model-training and inference data through practical quality, monitoring, and reproducibility practices.\nEffective collaboration between ML developers, wet-lab teams, domain experts, and distributed technical partners.\nExpansion of scalable data and modeling infrastructure across research programs and pipelines.\nTypical Candidate Profile\nThe ideal candidate combines strong data-engineering and MLOps expertise with enough scientific domain fluency to work effectively with ML developers, and experimental collaborators. They enjoy building reusable systems that make complex scientific data reliable, reproducible, and actionable for predictive modeling.\nCandidates may come from data science, data engineering, machine learning infrastructure, computational biology, computational chemistry, computational materials science, bioinformatics, or research informatics backgrounds. They are motivated by bridging scientific and engineering needs, supporting production-ready model use, and scaling technical solutions across discovery programs.\nOrganizational Impact\nThis role will build ML-amenable data pipelines for protein property data, mediate collaborations between ML developers and wet-lab teams, and scale data and modeling infrastructure across research programs and pipelines. By converting complex domain needs into maintainable, production-ready technical solutions, the Senior Data Scientist - Protein Data Pipelines will help accelerate reliable model development, deployment, and adoption across Large Molecule Discovery.\n.","description_format":"text","description_chars":7563,"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":["Prescription Drugs","Biologics & Biosimilars","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":0.568,"p_room":0.44,"age_days":61,"expected_fill_days":42,"reasons":["conf:1","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-11-10T06:52:35Z"},"pay":null,"html_url":"https://alion.io/job/amgen-senior-data-scientist-protein-data-pipelines","json_url":"https://alion.io/job/amgen-senior-data-scientist-protein-data-pipelines.json","meta":{"generated_at":"2026-11-10T09:40:36Z","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":22494,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-11T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"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-data-pipelines"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"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-data-pipelines"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"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-data-pipelines"},{"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-data-pipelines\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famgen-senior-data-scientist-protein-data-pipelines"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":577466}},{"tool":"search_jobs","arguments":{"company":"amgen.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":577466},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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