GE HealthCare – Hiring for Data Science Specialist @Bengaluru

Global healthcare technology leader GE HealthCare has announced an opening for the position of Data Science Specialist (AI Engineer) within its Digital Technology / IT division in Bengaluru, Karnataka.

This role is part of the AI-enabled Systems Engineering organization, focusing on developing intelligent machine learning, deep learning, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI applications to transform product lifecycle workflows, improve product quality, and enhance regulatory compliance.

Job Details

About Company

GE HealthCare is a leading global medical technology, pharmaceutical diagnostics, and digital solutions innovator. Operating across more than 160 countries, GE HealthCare enables clinicians to make faster, more informed decisions through intelligent devices, data analytics, and artificial intelligence.

With a core purpose to “create a world where healthcare has no limits,” the company’s Digital Technology and Systems Engineering teams drive the deployment of cutting-edge AI frameworks (including predictive analytics, LLMs, and computer vision) across healthcare devices and enterprise workflows.

Job Description

As a Data Science Specialist, you will work on large-scale engineering, quality, and product lifecycle datasets to build intelligent automation and decision-support solutions.

You will collaborate directly with Systems Engineers, Software Engineers, AI Scientists, and Verification & Validation teams to design, evaluate, and deploy machine learning and Generative AI applications (such as RAG systems and LLM-driven knowledge management tools) that enhance engineering productivity and patient outcomes.

Requirements & Qualifications

1. Educational Qualifications & Experience

• Degree: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related STEM discipline.
• Experience: 0 to 2 years of relevant hands-on experience in AI, Data Science, Software Engineering, or Advanced Analytics (Freshers with strong project/internship experience are eligible).

2. Technical Competencies

• Programming & Libraries: Strong proficiency in Python and core data science packages (Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow).
• Generative AI & LLMs: Working knowledge or project exposure to Generative AI frameworks, LLM APIs (OpenAI / Azure OpenAI), LangChain, LlamaIndex, Semantic Kernel, and Agentic AI workflows.
• Core Fundamentals: Solid grasp of Machine Learning algorithms, statistics, probability, data structures, algorithms, and model evaluation metrics.
• Data & Cloud: Knowledge of SQL, data manipulation techniques, version control (Git), and exposure to cloud environments (Azure, AWS, or GCP) or visualization tools (Power BI, Tableau).

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Roles and Responsibilities

1. AI & Machine Learning Model Development

• Design, train, evaluate, and deploy predictive, descriptive, and generative AI models to solve engineering and business problems.
• Develop Retrieval-Augmented Generation (RAG) pipelines, Agentic AI tools, and LLM-driven applications for workflow automation and knowledge discovery.
• Perform exploratory data analysis (EDA), data cleaning, feature engineering, and statistical modeling on large-scale structured and unstructured datasets.

2. Systems Integration & Cross-Functional Collaboration

• Partner with Systems Engineers and Verification & Validation teams to build AI-driven tools that streamline test management and quality tracking.
• Create interactive dashboards, analytical reports, and technical documentation to communicate model performance and insights.

3. AI Governance & Quality Assurance

• Participate in responsible AI practices, model governance, traceability, and validation processes required for regulated medical technology environments.

Role Summary

A Data Science Specialist at GE HealthCare functions at the intersection of AI innovation and healthcare systems engineering. Instead of purely theoretical data modeling, you will build production-ready AI solutions, RAG pipelines, and automated analytics tools that directly optimize how medical technology products are engineered and validated.

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Company Culture & Insights

GE HealthCare’s Bengaluru Innovation Centre offers an open, inclusive, and collaborative environment. Employees work alongside senior AI scientists and domain experts on high-impact projects. The culture prioritizes continuous learning, innovation, responsible AI governance, and cross-functional career development.

Why We Recommend This Job

1. Global Healthcare Leader: Work directly on AI solutions that impact medical devices and digital technology globally.
2. Generative AI Exposure: Hands-on development using modern LLM stacks (RAG, Agentic AI, Azure OpenAI, LangChain) in an enterprise environment.
3. Great Entry-Level Opportunity: Ideal for early-career engineers (0–2 years experience) looking to build strong equity in AI/ML engineering.

Career Growth Potential

Data Science Specialist ➔ Senior Data Scientist ➔ Staff AI Scientist ➔ Senior Staff / Principal AI Architect

Skills You’ll Gain

Salary & Benefits Info

• Compensation: Competitive salary package aligned with top-tier technology & healthcare product companies in Bengaluru.
• Health & Wellness: Comprehensive group health insurance, wellness programs, and parental leaves.
• Perks & Facilities: Flexible work options, learning subsidies, continuous training certifications, and retirement benefits (PF/Gratuity).

How to Apply?

1. Visit the official GE HealthCare careers page using the link below.
2. Click on the Apply Now button.
3. Create an account or sign in via Workday.
4. Upload your updated Resume/CV and complete the application form with your academic and technical details.
5. Submit your job application.

Official Application Link: Apply Now

Frequently Asked Interview Questions (With Answers & Solution Guidelines)

1. Explaining Retrieval-Augmented Generation (RAG)
   • Question: “How does a RAG architecture improve LLM responses when querying unstructured engineering documentation?”
   • Answer & Solution Context: Explain that standard LLMs can suffer from hallucinations or outdated knowledge. RAG combines a retriever (converting documents into vector embeddings stored in a vector database) with a generator (LLM). When a query comes in, the system retrieves top-$k$ relevant context chunks via semantic similarity search and injects them into the prompt, ensuring grounded, accurate, and traceable responses.
2. Handling Imbalanced Datasets in Classification
   • Question: “If defect data in quality systems represents only 2% of the overall dataset, how do you handle class imbalance when building an ML model?”
   • Answer & Solution Context: Discuss resampling techniques (SMOTE / ADASYN for oversampling minority classes, or random undersampling), algorithm-level adjustments (using class weights in loss functions), and evaluating performance with Precision-Recall curves, F1-Score, or ROC-AUC rather than standard accuracy.
3. Evaluating Generative AI & LLM Performance
   • Question: “What metrics or frameworks do you use to evaluate the quality and accuracy of an LLM or RAG pipeline?”
   • Answer & Solution Context: Detail evaluation frameworks like RAGAS or TruLens, focusing on metrics such as Faithfulness (is the answer grounded in context?), Answer Relevance (does it address the prompt?), Context Precision, and human-in-the-loop validation.

Behavioral & Culture Questions (With Guidance & Model Answers)

1. Working in Cross-Functional Teams
   • Question: “How do you explain complex machine learning model outputs or AI metrics to non-technical stakeholders like quality assurance managers?”
   • Guidance & Answer Strategy: Use the STAR method. Emphasize translating technical metrics (like ROC-AUC or loss) into business impact (e.g., reducing manual defect review time by 30%). Highlight using clear visual dashboards and intuitive explanations.
2. Continuous Learning in Rapidly Evolving AI
   • Question: “Generative AI frameworks evolve rapidly. How do you stay updated with new papers and tools while delivering ongoing project tasks?”
   • Guidance & Answer Strategy: Describe structured personal habits—such as regularly tracking arXiv papers, experimenting with open-source frameworks on GitHub/Kaggle, and participating in internal technical knowledge-sharing sessions.