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Popular mid-level data science role, metro location, broad skillset and known global brand increase applicant competition.
Core ML engineering and MLOps skills are broadly transferable across industries.
Explicit 3–6 year requirement plus mandatory Python/PySpark/SQL and MLOps skills make filters moderately strict.
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Implement, test, validate, monitor, and report on a range of analytical solutions using large datasets.
Perform model scoring, result validation, impact analysis, and periodic model monitoring to ensure accuracy and operational stability.
Develop and support automation and AI-driven tools to streamline analytics processes and support MLOps/LLMOps for AI/ML deployment and governance.
3 to 6 years of relevant experience in Data Analytics or Data Science roles.
Strong programming skills in Python, PySpark, and SQL.
Familiarity with statistical and machine learning techniques and AI/ML model monitoring and automation frameworks.
Knowledge of LLM tools, GenAI concepts, and modern AI techniques.
Experienced in handling large datasets and building production-ready AI/ML solutions with operational monitoring.
Skilled in developing automation and AI-driven operational efficiency initiatives within analytics.
Familiar with MLOps/LLMOps processes and use of custom-built frameworks for analytics and AI/ML deployments.