{"id":4060927,"url":"https://alion.io/job/thermo-fisher-scientific-software-engineer-algorithm-development","title":"Software Engineer – Algorithm Development","company":{"id":7922,"name":"Thermo Fisher Scientific","domain":"thermofisher.com","url":"https://alion.io/company/thermo-fisher-scientific","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":351,"ghost_share":0,"stale_share":0.692,"repost_share":0.006,"time_to_fill_p50_days":31,"computed_at":"2026-11-11T05:49:22Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SciPy","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"Delta Lake","optional":true},{"name":"Docker","optional":true},{"name":"Feature Store","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-11-11T08:35:55Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-11T08:35:55Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Work Schedule\nStandard (Mon-Fri)Environmental Conditions\nOfficeJob Description\nRole Overview\nWe are looking for an experienced Software Engineer with strong skills in Python, algorithm development, scientific computing, and data-driven solution development.\nThe ideal candidate will contribute to the design, implementation, validation, and productization of algorithms that transform complex diagnostics and operational data into actionable insights. The role will work closely with senior engineers, domain experts, data engineers and platform teams to develop solutions for data analysis, signal and image processing, predictive analytics, and machine learning.\nAs our data platform evolves from the current AWS-based environment toward Databricks, the candidate will also contribute to migrating and modernizing algorithmic and analytical workloads. This includes adapting Python-based computational workflows for distributed processing, developing reusable features for analytics and machine learning, and applying modern ML development practices on Databricks.\nThis role does not own core data ingestion or ETL pipelines. It focuses on the analytical and algorithmic layer that consumes curated data provided by the data exploration/engineering platform.\nKey Responsibilities\nDevelop and maintain algorithms for processing, analyzing, and modeling machine diagnostics and operational data.\nBuild Python-based computational workflows using appropriate scientific computing and data analysis.\nAnalyze complex datasets to identify patterns, derive useful features, and support predictive or diagnostic insights.\nDevelop and improve algorithms in areas such as signal processing, image analysis, anomaly detection, predictive analytics, and other data-driven applications.\nPerform algorithm testing, validation, benchmarking, and performance evaluation against defined criteria.\nCollaborate with domain experts and data scientists to translate technical and scientific problems into practical analytical solutions.\nWork with data engineers to understand data structures, data quality, and curated datasets required by algorithms.\nDevelop reusable, modular, testable, and maintainable Python components.\nImprove the performance and scalability of analytical workloads under the guidance of senior engineers and platform specialists.\nContribute to the migration of existing algorithmic and analytical workloads from the current AWS-based data platform to Databricks.\nAdapt existing Python and analytical workloads to work with Databricks, Spark/PySpark, Delta-based datasets, and distributed compute where appropriate.\nDevelop and maintain reusable feature-generation logic for model development and analytical applications.\nApply structured ML lifecycle practices such as experiment tracking, reproducibility, model evaluation, versioning, and deployment.\nSupport the productionization of algorithms and machine learning models through repeatable and automated workflows.\nParticipate in code reviews, technical design discussions, estimation, testing, and implementation planning.\nFollow software engineering practices including source control, automated testing, CI/CD, documentation, and observability.\nContinuously build knowledge of Databricks, distributed processing, and modern ML engineering practices.\nRequired Technical Skills\n5-8 years of professional experience in software engineering, algorithm development, scientific computing, data science, machine learning, or related technical disciplines.\nStrong programming skills in Python with experience developing reliable and maintainable software.\nGood experience with scientific computing and data analysis libraries such as NumPy, pandas, SciPy, or equivalent technologies.\nExperience implementing data-processing, analytical, mathematical, or predictive algorithms.\nExperience working with complex datasets and developing data-driven models, features, or analytical workflows.\nExperience in one or more areas such as machine learning, predictive analytics, signal processing, image processing, time-series analysis, optimization, or statistical modeling.\nGood understanding of machine learning fundamentals including feature engineering, model training, validation, evaluation, and inference.\nExperience converting exploratory or prototype code into structured, testable, and maintainable implementations.\nGood understanding of software engineering fundamentals including modular design, object-oriented programming, testing, maintainability, and performance considerations.\nExperience working with cloud-based compute or data environments, preferably AWS.\nWorking knowledge of SQL and experience accessing and analyzing structured datasets.\nBasic understanding of scalable or distributed data-processing concepts.\nExperience with source control systems such as Git.\nFamiliarity with automated testing, CI/CD, Agile, DevOps, or modern software development practices.\nAbility to work effectively with software engineers, data engineers, data scientists, domain experts, and platform teams.\nPreferred Skills\nExposure to Apache Spark or PySpark.\nExperience with Databricks or another cloud-based data and machine learning platform.\nExperience working with notebooks, managed compute environments, or cloud-based analytical workflows.\nFamiliarity with Delta Lake or lakehouse-based data architectures.\nExposure to MLflow or similar tools for experiment tracking and model management.\nExperience developing reusable features for machine learning or analytical models.\nFamiliarity with feature stores, model registries, or similar ML lifecycle concepts.\nExposure to Unity Catalog or similar data and model governance approaches.\nExperience supporting batch or real-time model inference.\nUnderstanding of MLOps concepts such as model versioning, reproducibility, automated testing, deployment, and monitoring.\nExperience optimizing Python-based data or analytical workloads.\nExposure to containerization technologies such as Docker.\nExperience integrating algorithms or analytical models into APIs, applications, or digital services.\nExperience working with scientific instrumentation, engineering data, image data, sensor data, or other complex technical datasets.\nExperience in industrial R&D, scientific, engineering, or product-oriented environments.\nBehavioral Competencies\nStrong analytical and problem-solving skills.\nAbility to work effectively on technically complex problems with guidance from senior engineers and domain specialists.\nWillingness and ability to learn new technologies as the data platform evolves.\nStrong ownership of assigned technical deliverables.\nAbility to apply appropriate engineering judgment when choosing between different analytical or computational approaches.\nGood communication skills and ability to explain technical approaches, assumptions, and results clearly.\nAbility to collaborate effectively across software engineering, data engineering, data science, platform engineering, and domain teams.\nAbility to work independently on defined technical problems while contributing effectively within a team.\nStrong focus on software quality, reproducibility, performance, and maintainability.\nCuriosity and willingness to develop deeper skills in Databricks, distributed processing, and ML engineering.\nEducation\nBachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Physics, Electronics, Engineering, or a related technical discipline.","description_format":"text","description_chars":7456,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Diagnostic Tests & IVD","Clinical Trial Services (CRO)","CRO & CDMO Services","APIs & Drug 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