{"id":3879228,"url":"https://alion.io/job/siemens-junior-data-scientists","title":"Junior Data Scientists","company":{"id":131,"name":"Siemens","domain":"siemens.com","url":"https://alion.io/company/siemens","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":100,"open_postings":32,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":13,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Data Science","role_family":"Data Science","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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"DICOM","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Keras","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Neo4j","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Verification & Validation","optional":false}],"status":"live","first_seen_at":"2026-11-06T17:21:33Z","employer_posted_date":null,"last_verified_at":"2026-11-06T17:21:33Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Role\n\nJob Requirements \n\nTo contribute to the end-to-end development and product lifecycle management of artificial intelligence and machine learning (AI/ML) systems as part of an R&D organization. The Junior Data Scientist supports the definition of intended algorithm performance, the development and curation of representative data and annotations, the development of AI/ML algorithms, and the generation of scientific evidence demonstrating stand-alone algorithm performance.\n\nThe Candidate Is Expected To\n\nApply scientific, statistical, machine learning, and software engineering methods to develop and evaluate AI/ML algorithm components for defined product use cases.\nWork with experienced Data Scientists, product management, clinical or domain experts, software developers, and quality and regulatory functions to translate customer needs and the intended use into measurable algorithm performance targets.\nSupport the definition, sourcing, curation, documentation, and maintenance of representative data cohorts and annotations required for training, tuning, and evaluation.\nPlan and execute reproducible scientific evaluations that provide objective evidence of algorithm performance, limitations, and suitability for the intended use.\nContribute the Data Science work products and evidence required for integration of AI/ML algorithm components into products and for their verification, validation, regulatory release, and lifecycle monitoring.\n\nWhat are my responsibilities\n\nAs a Junior Data Scientist, You Are Required To\n\nSupport the finalization of product and algorithm concepts by translating customer needs, intended use, clinical or technical requirements, and applicable regulatory considerations into measurable state-of-the-art performance targets.\nContribute to the definition of the AI/ML development and scientific evaluation strategy for assigned algorithm components under the guidance of experienced Data Scientists.\nPerform exploratory data analysis and data risk assessments to identify data gaps, quality issues, potential bias, confounding factors, outliers, and limitations that may affect algorithm development or evaluation.\nDefine and document data needs for training, tuning, and evaluation, including relevant subgroups and acceptance criteria, so that data cohorts are representative of the intended use and aligned with the product and regulatory strategy.\nSupport the specification of annotations, acceptance criteria, and quality controls; collaborate with qualified internal or external suppliers on sourcing data and annotations.\nCurate, version, maintain, and document representative data cohorts and ensure data provenance, traceability, quality, and fitness for purpose together with data management stakeholders.\nDesign, implement, train, tune, and technically characterize AI/ML algorithm components using appropriate machine learning, deep learning, statistical, or generative AI methods.\nDefine scientifically justified metrics, evaluation methods, test cases, and acceptance criteria to assess stand-alone algorithm performance, including relevant aspects such as accuracy, robustness, reliability, generalizability, explainability, bias, and subgroup performance.\nImplement and execute reproducible bench tests on representative evaluation cohorts and relevant subgroups as part of the verification and validation strategy.\nAnalyze and interpret evaluation results, investigate failure modes, document residual limitations, and derive evidence-based recommendations for algorithm improvement and product decisions.\nDocument development and evaluation activities and contribute to Data Science work products such as data management information, annotation specifications, scientific evaluation test cases, and scientific evaluation reports.\nProvide scientific evidence and technical input for product reviews and regulatory submissions and support responses to questions concerning data, annotations, evaluation methods, and stand-alone algorithm performance.\nSupport the design and execution of clinical investigations, reader studies, or other studies when additional evidence is required by the product or regulatory strategy.\nCollaborate with software engineering and MLOps functions to establish reproducible pipelines, versioned artifacts, automated tests, controlled deployment, and integration of algorithm components into the product.\nHelp define and implement monitoring metrics for detecting data anomalies, outliers, and algorithm performance drift across the product lifecycle.\nRegularly review advances in Data Science and AI and assess their applicability to the development and evaluation of AI/ML products.\n\nQualification\n\nMaster's degree in Computer Science, Data Science, Statistics, Mathematics, Biomedical Engineering, Medical Informatics, or a related quantitative or engineering field. A Ph.D. is welcome but not required. The candidate should have completed university-level coursework or equivalent practical training in:\n\nProbability, statistics, and experimental design\nArtificial intelligence, machine learning, and deep learning\nLinear algebra, optimization, and numerical methods\nData structures, algorithms, and software programming\nModel evaluation, scientific methodology, and interpretation of empirical results\n\nExperience Level\n\nThis is a junior R&D position. Relevant experience may have been gained through academic research, a thesis, internships, working-student assignments, or up to approximately three years of professional work in Data Science, machine learning, AI research, or AI/ML product development. Demonstrated hands-on experience in developing and scientifically evaluating AI/ML models is more important than a specific number of years.\n\nDesired Knowledge & Experience\n\nSound understanding of machine learning, deep learning, statistical analysis, experimental design, and model evaluation.\nActive programming skills in Python and practical experience with libraries and frameworks such as NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or Keras.\nHands-on experience with the development workflow of AI/ML models, including data preparation, training, tuning, evaluation, error analysis, reproducibility, and documentation.\nAbility to define meaningful performance metrics, evaluation cohorts, subgroup analyses, test methods, and acceptance criteria for the intended use of an AI/ML algorithm.\nUnderstanding of data quality, representativeness, bias, annotation quality, data provenance, and traceability in AI/ML development.\nKnowledge of Machine Learning Operations (MLOps) concepts and tooling, including source and data versioning, reproducible pipelines, experiment tracking, automated testing, CI/CD, deployment, and monitoring.\nHands-on exposure to current Large Language Models and Generative AI, including foundation models, prompting, retrieval-augmented generation, and model adaptation or fine-tuning.\nKnowledge of Agentic AI, including tool-using models, multi-step workflows, orchestration, memory, and autonomous or semi-autonomous decision paths, is an advantage.\nExperience or strong interest in the systematic evaluation of LLM, Generative AI, and Agentic AI systems, including task-specific benchmarks, groundedness and factuality, robustness, reproducibility, human evaluation, safety and guardrail testing, tool-use correctness, and analysis of failure modes.\nExperience with databases and query languages such as SQL; knowledge of Kusto Query Language, Snowflake, or graph databases such as Neo4j is an advantage.\nExposure to cloud-based data and AI environments, preferably Microsoft Azure, such as Azure Blob Storage, Azure Databricks, Azure Data Factory, Azure Machine Learning, Azure Monitor, or Azure DevOps.\nExperience with structured and unstructured data, for example machine logs, event data, Parquet files, text data, or multimodal data.\nKnowledge of healthcare, medical technology, clinical data, or interoperability standards such as DICOM is preferred.\nBasic project management skills and familiarity with working in cross-functional R&D or product-development teams.\n\nRequired Soft Skills & Other Capabilities\n\nScientific mindset, strong analytical ability, and attention to detail.\nCommitment to objective evidence, reproducibility, traceability, data integrity, and responsible AI development.\nCuriosity and resilience to investigate unexpected results, challenge assumptions, and improve models based on evidence.\nCollaborative approach to working with Data Scientists, domain experts, software engineers, product management, and quality and regulatory stakeholders.\nAbility to work independently within an agreed scope and to seek guidance when technical, scientific, product, or regulatory implications require alignment.\nClear written and verbal communication skills in English, including the ability to explain methods, results, uncertainties, limitations, and risks to technical and non-technical stakeholders.","description_format":"text","description_chars":8972,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["CAD, PLM & BIM Software","Rail Transport","Building Automation & Controls","Manufacturing & Industrial Software"],"lifecycle":[{"event":"open","at":"2026-11-06T17:33:15Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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