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Junior Data Scientist (Healthcare AI & R&D) — Siemens Healthineers
Company: Siemens Healthineers
Division: Research & Development (R&D) — Artificial Intelligence & Machine Learning Systems
Role Title: Junior Data Scientist
Location: Bengaluru, Karnataka, India
Work Mode: Hybrid / Office-Based
Employment Type: Full-Time
Experience Level: 0 to 3 Years of professional experience, academic research, thesis work, or R&D internships
Educational Requirement: Master’s degree or PhD in Computer Science, Data Science, Statistics, Mathematics, Biomedical Engineering, Medical Informatics, or a related quantitative engineering discipline
Core Tool Stack: Python, PyTorch, TensorFlow, Keras, scikit-learn, NumPy, Pandas, SciPy, SQL, MLOps, Microsoft Azure (Azure ML, Databricks, Data Factory), DICOM
Emerging Tech Stack: Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Neo4j, Snowflake, Kusto Query Language (KQL)
About Siemens Healthineers
Siemens Healthineers is a global healthcare technology leader driving medical innovations that enable healthcare providers worldwide to expand precision medicine, transform care delivery, and improve patient experience. Siemens Healthineers’ premier R&D center in Bengaluru is at the forefront of digital health, engineering state-of-the-art AI algorithms, medical imaging solutions, diagnostic software, and cloud-native clinical decision support platforms.
Role Overview
Siemens Healthineers is recruiting an evidence-driven Junior Data Scientist to join its R&D division in Bengaluru.
In this medical AI product development role, you will contribute to the end-to-end lifecycle of artificial intelligence and machine learning components. You will collaborate with clinical experts, software developers,and regulatory teams to design, train, and scientifically validate algorithms, curate representative clinical data cohorts, fine-tune Generative AI/LLM models, and build reproducible MLOps pipelines for regulatory submission and product release.
Key Responsibilities
Algorithm Engineering & Model Development
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AI/ML Model Design: Apply scientific, statistical, deep learning, and software engineering methods to design, train, and tune AI/ML algorithm components for defined clinical product use cases.
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GenAI & Agentic AI: Explore and adapt Generative AI, LLM foundation models, Retrieval-Augmented Generation (RAG), and tool-using Agentic AI workflows for healthcare analytics.
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Exploratory Analysis & Data Risk: Conduct exploratory data analysis to identify data gaps, quality issues, potential bias, confounding factors, and outliers in medical datasets.
Scientific Evaluation, Evidence & Regulatory Compliance
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Cohort Curation & Provenance: Define, curate, annotate, version, and document representative data cohorts (including DICOM, text, and multimodal data) ensuring strict traceability and quality controls.
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Verification & Benchmarking: Design and execute reproducible bench tests and subgroup analyses to evaluate algorithm accuracy, robustness, generalizability, explainability, and bias.
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Regulatory Submissions: Prepare Data Science work products, annotation specifications, test cases,and scientific evaluation reports required for regulatory clearance, clinical trials, and product release.
MLOps & Lifecycle Integration
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Pipeline Automation: Collaborate with MLOps and software engineering teams to establish reproducible CI/CD pipelines, versioned artifacts, automated testing, and controlled model deployment.
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Lifecycle Monitoring: Define and implement monitoring metrics to detect data drift, anomalies, and algorithm degradation across the post-deployment product lifecycle.
Candidate Qualifications & Requirements
Required Education & Core Training
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Education: Master’s degree or PhD in Computer Science, Data Science, Statistics, Mathematics,Biomedical Engineering, Medical Informatics, or a related quantitative field.
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Foundational Coursework: University-level coursework in probability, experimental design, linear algebra, numerical optimization, data structures, and scientific model evaluation.
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Experience Level: 0 to 3 years of hands-on experience gained through academic research, master’s/PhD thesis, internships, or professional AI/ML R&D work.
Desired Technical Skills & Frameworks
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Programming & ML Libraries: Strong active coding skills in Python using libraries such as NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or Keras.
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Database & Cloud Ecosystem: Proficiency in SQL; exposure to Microsoft Azure cloud services (Azure ML, Databricks, Blob Storage, Azure DevOps) or query tools like KQL/Snowflake.
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GenAI & LLM Evaluation: Exposure to LLMs, RAG architectures, prompt engineering, and evaluation techniques (factuality, groundedness, safety/guardrail testing).
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Domain Knowledge (Preferred): Familiarity with healthcare data, clinical workflows, machine logs, or medical interoperability standards (DICOM).
Key Soft Skills
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Scientific rigor, analytical mindset, and commitment to objective evidence, reproducibility, and data integrity.
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Excellent written and verbal communication skills in English to convey complex algorithm limitations to clinical, technical, and regulatory stakeholders.
Role Summary
| Attribute | Details |
| Job Title | Junior Data Scientist |
| Company | Siemens Healthineers |
| Division | R&D — AI & Machine Learning Systems |
| Location | Bengaluru, Karnataka, India |
| Experience | 0–3 Years (Master’s / PhD research or industry entry-level) |
| Primary Skill Stack | Python, PyTorch/TensorFlow, scikit-learn, SQL, Azure ML, MLOps, DICOM |
| Emerging Skill Stack | Generative AI, LLMs, RAG, Agentic AI, Scientific Model Evaluation |
| Core Deliverables | Clinical Algorithm Training, Scientific Evaluation Reports, Regulatory Artifacts, MLOps Pipelines |
