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Strong Tier-1 brand, metro location, mid-level role, and broad ML skillset increase candidate competition.
Healthcare-specific data standards and model governance requirements reduce cross-industry transferability.
Multiple mandatory ML frameworks, healthcare data standards, and a 6+ years requirement make filters strict.
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Analyze large, complex healthcare datasets to deliver actionable insights using statistical and advanced ML techniques.
Design, develop, and deploy production-grade ML/DL models and solutions with Python and SQL, including feature engineering, evaluation, and explainability.
Lead model governance practices, collaborate with data engineering, and communicate technical insights to both technical and non-technical stakeholders.
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field.
Minimum 6 years experience as Data Scientist or in a relevant stream.
Hands-on experience with AutoML platforms and deep learning frameworks (TensorFlow, PyTorch, Keras).
Proficiency in Python, R, and SQL; familiarity with synthetic data generation tools.
Experienced in healthcare data domain with knowledge of ICD, CPT, NDC, SNOMED, LOINC, FHIR, and HL7 standards.
Skilled in diverse ML methods including classical ML, advanced statistical modeling, deep learning (NLP, vision), recommender systems, and graph ML.
Able to independently manage end-to-end model development lifecycle including governance, monitoring, and interdisciplinary collaboration.