Analyst II, Data Science (R-19664)

Key Responsibilities:

Work across a range of analytical solutions involving implementation, testing, validation, documentation, monitoring, investigations, and reporting.

Extract, analyze, and validate large datasets from various data sources using tools such as Python, PySpark, SQL, and Hadoop to support analytics implementations and operational activities. Perform model scoring, result validation, impact analysis, score investigations, and periodic model monitoring to ensure accuracy and operational stability.

Develop and support automation solutions, AI-driven tools, and operational efficiency initiatives to streamline analytical processes and reduce manual effort.

Utilize custom-built frameworks and platforms for analytics implementations, validations, monitoring, reporting, and delivery activities.

Support MLOps/LLMOps processes for deployment, monitoring, observability, validation, and governance of AI/ML solutions.

Create and maintain technical documentation, implementation artifacts, operational procedures, and knowledge repositories.

Key Skills:

3 to 6 years of relevant experience in Data Analytics / Data Science roles.

Strong programming skills in Python, PySpark, and SQL to manipulate data and develop production-ready solutions.

Knowledge of LLM tools, GenAI concepts, and modern AI techniques.

Sound knowledge and exposure to the application of Statistical and Machine Learning techniques.

Familiarity with AI/ML model monitoring, observability, automation frameworks, and GenAI applications.

Exposure to workflow orchestration, process automation, or AI-driven operational efficiency solutions will be an added advantage.

Exposure to LLM agents/tools and certifications in GenAI will be an added advantage.

Ability to interpret and translate data into meaningful business insights.

Excellent verbal, written, communication, and presentation skills.