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Tier-1 employer, common Data Scientist title, and mid-level experience create medium competition.
Requires advanced ML/AI and healthcare-related deployment experience, limiting cross-industry transferability somewhat.
Explicit 6+ years requirement plus mandatory ML, MLOps, and production deployment skills increases shortlisting strictness.
Design, develop, validate, and deploy machine learning and predictive analytics models including classification, regression, forecasting, and recommendation systems to solve complex business problems.
Operationalize AI/ML solutions including monitoring model performance, handling model drift, and implementing continuous improvement and retraining strategies.
Apply advanced techniques such as NLP, Generative AI, LLMs, RAG, and agentic AI frameworks for real-world business applications, translating business requirements into scalable ML solutions.
6+ years of experience in Data Science, Machine Learning, Applied Statistics, or related field.
Proficiency in Python, SQL, and ML frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
Solid understanding of machine learning algorithms, statistical modeling, model validation, and risk management.
Familiarity with MLOps practices including CI/CD pipelines, model monitoring, and production deployment frameworks.
Experienced in end-to-end ML model lifecycle from development to production deployment in a business context.
Demonstrated ability to apply and operationalize cutting-edge AI technologies like Generative AI, LLMs, and RAG architectures within analytics solutions.
Skilled in translating complex business challenges into analytical frameworks leveraging both traditional ML and advanced AI methods.