Data Science Specialist

GE Healthcare’s interview process typically starts with an online aptitude/technical test, followed by a deep‑dive technical interview focusing on Python, machine learning concepts, and real‑world problem solving. The final HR round assesses cultural fit and communication skills. Candidates should be ready to discuss past projects, explain model choices, and demonstrate analytical thinking.

GE Healthcare promotes a collaborative, innovation‑driven culture where employees are encouraged to experiment and learn continuously. The company offers structured growth programs, mentorship, and a healthy work‑life balance, with flexible work arrangements and a focus on employee well‑being.

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering or related STEM discipline; Minimum 60% aggregate marks; Fresh graduates of 2024‑2026 batches; No active backlogs at the time of joining.

Round 1: Online Assessment → Round 2: Technical Interview → Round 3: HR Interview

GE Healthcare is a global leader in medical technology and digital health solutions, serving more than 150 countries with innovative products that span imaging, diagnostics, patient monitoring, and AI‑driven care platforms. The company’s mission – to create a world where healthcare has no limits – drives a culture of relentless innovation, collaboration across disciplines, and a deep commitment to improving patient outcomes. With a strong presence in India, GE Healthcare offers a vibrant ecosystem where engineers, data scientists, clinicians and business leaders work together to solve some of the most complex challenges in healthcare delivery. As a Data Science Specialist (AI Engineer) in the AI‑enabled Systems Engineering organization, you will be at the forefront of transforming product development pipelines using artificial intelligence, machine learning, and generative AI. The role blends data engineering, model development, and domain knowledge to build intelligent tools that boost engineering productivity, enhance product quality, and ensure regulatory compliance. You will collaborate closely with Systems Engineers, Software Engineers, Verification & Validation Engineers, and domain experts to embed AI into everyday engineering workflows. **Key Responsibilities** 1. Design, develop, and deploy AI/ML models that address engineering and business problems such as defect prediction, test‑result analysis, and knowledge‑base automation. 2. Perform exploratory data analysis, statistical modeling, and feature engineering on large structured and unstructured datasets sourced from requirements, quality systems, and operational logs. 3. Build Retrieval‑Augmented Generation (RAG) pipelines, agentic AI assistants, and LLM‑based applications for engineering knowledge management. 4. Create dashboards, visualizations, and technical documentation to communicate model insights and performance metrics to cross‑functional stakeholders. 5. Participate in data preparation, cleansing, validation, and quality‑assessment activities to ensure robust training data. 6. Contribute to responsible AI practices, including model governance, traceability, and validation in regulated environments. 7. Stay abreast of emerging AI technologies and evaluate their applicability to healthcare engineering challenges. 8. Support prototype development, proof‑of‑concept experiments, and iterative improvement of AI‑enabled tools. **Tech Stack**: Python, Pandas, NumPy, Scikit‑learn, TensorFlow/PyTorch, LangChain, LlamaIndex, Azure/OpenAI APIs, SQL, Git, Power BI/Tableau, cloud platforms (Azure/AWS/GCP). **Growth Path**: Starting as a Data Science Specialist, you can progress to Senior Data Scientist, AI Solutions Architect, or Engineering Lead, with opportunities to lead multi‑disciplinary AI projects and influence product strategy at a global level. **Why Join GE Healthcare?** The company offers exposure to cutting‑edge AI research applied to real‑world medical devices, a collaborative multicultural environment, strong mentorship programs, and a clear career trajectory. Freshers gain hands‑on experience with large‑scale datasets, regulated‑industry compliance, and the chance to make a tangible impact on patient care worldwide.