{"id":2326659,"url":"https://alion.io/job/alector-senior-associate-scientist-computational-biology","title":"Senior Associate Scientist, Computational Biology","company":{"id":2939952,"name":"Alector","domain":"alector.com","url":"https://alion.io/company/alector","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"Science & Research","role_family":"Science & Research","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["South San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":130000,"max":140000,"currency":"USD","period":"year","gross":null,"usd_annual":140000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"dplyr","optional":false},{"name":"Git","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":true},{"name":"R","optional":true},{"name":"Shiny","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-01T21:57:06Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-20T17:42:42Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"Alector is seeking a Senior Associate Scientist, Computational Biology, to join our dynamic team. You will play a key role in advancing our understanding of the biology underlying neurodegenerative diseases, improving our computational pipelines, and supporting the discovery of new therapeutic targets.\nIn this role, you will analyze and interpret complex biological data, with a particular focus on high-content imaging and cell segmentation, alongside high-dimensional multi-omics datasets. You will collaborate with scientists across R&D to design experiments, analyze results, and translate data into meaningful biological insights, while also helping to modernize our pipelines with emerging AI and machine-learning tools.\nKey Goals & Accountabilities\nAdvance image-based research!Develop, benchmark, and apply computational methods for high-content imaging, including image segmentation, feature extraction, and quantitative single-cell tracking analysis from a broad variety of samples. Evaluate and integrate deep-learning segmentation approaches to improve accuracy and throughput of our models.\n\nModernize our pipelines with AI!Identify, evaluate, and incorporate new AI and machine-learning tools into imaging and multi-omics workflows, improving automation, reproducibility, and the speed of turning raw data into insight.\n\nTurn complex data into biological insights!Analyze imaging and multi-omics datasets and partner with scientists across R&D to address key biological questions and inform experimental design.\n\nMake scientific data accessible!Manage and organize data from internal and published studies and develop interactive data-exploration portals for internal use.\n\nBuild efficient and reproducible workflows!Apply best practices in code and data management, including workflow orchestration, version control, and documentation, while identifying opportunities for continuous improvement.\n\nCommunicate findings clearly!Present analytical results in verbal, visual, and written formats to cross-functional audiences with varying levels of technical expertise.\n\nRequired Qualifications\nM.S. with at least three years of relevant experience or Ph.D. with at least one year of relevant experience in biotechnology, computational biology, data science, bioinformatics, or a related field.\n\nHands-on experience analyzing high-content imaging data, including cell segmentation, feature extraction, and qualification, as well as high-dimensional biological datasets, such as bulk or single-cell RNA sequencing or proteomics.\n\nExperience applying deep-learning and AI-based methods to image analysis, particularly cell segmentation (e.g., Cellpose, MEDIAR).\n\nProficiency in R and Python, including experience with relevant tools and packages such as DESeq2, Seurat, Scanpy, dplyr, and tidyverse for statistical modeling and data analysis.\n\nFamiliarity with shell scripting, version control using Git, and cloud-computing environments such as AWS.\n\nA fundamental understanding of biology and the ability to apply computational approaches to biological questions.\n\nExcellent written and verbal communication skills, with the ability to explain complex analyses to both technical and nontechnical audiences.\n\nPreferred Qualifications\nExperience with deep-learning frameworks such as TensorFlow or PyTorch and interest in evaluating emerging AI tools for biological data analysis.\n\nExperience building or optimizing image-analysis pipelines for high-content or microscopy data.\n\nExperience mining and analyzing publicly available biological datasets.\n\nExperience developing dashboards or interactive visualization tools using Shiny, Dash, or similar platforms.\n\nA fundamental understanding of neuroscience or neurodegenerative disease 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