Data Science_Mumbai

Hiring Data Science_Mumbai

For this JD, a screening should capture only whether each

CV explicitly mentioned

• 2–5 years in data analysis / data quality / reconciliation / investment-data operations
• Advanced Excel, especially formulas and Power Query
• SQL for extraction/investigation
• Large-dataset analysis and explicit data-quality checks
• Reconciliation / exception management / break investigation / root-cause analysis

Benchmark/index exposure

MSCI, FTSE Russell, S&P, Rimes, Ultumus

Skills

Digital

Microsoft Azure~Digital

Data Science~Digital

Machine Learning~AI & Gen AI - Products & Tools,Machine learning, Python, R, power bi, SQL

Experience Required

7+

Desired Competencies (Technical/Behavioral Competency)

You will be part of Data Analytics & Foundational Platforms within Wealth Management Americas, focusing on the development of statistical, machine learning and Generative AI solutions that enable better decision-making and improve business workflows.

We believe diversity strengthens our organization and are committed to fostering an inclusive environment that drives better outcomes for our people and our clients.

We're looking for a hands-on

GenAI Data Scientist to

• design, develop and test Generative AI and machine learning solutions under the guidance of senior data scientists and technical leads
• contribute to the end-to-end development of AI applications, from experimentation and prototyping through testing and production deployment
• build applications using large language models, retrieval-augmented generation, semantic search and agent-based approaches
• develop and maintain evaluation frameworks to measure the quality, reliability and consistency of LLM applications
• prepare, explore and analyze structured and unstructured data for modelling and application development
• experiment with prompting, retrieval strategies, embeddings, model configuration and fine-tuning techniques
• write clear, maintainable and reusable Python code, following software engineering and reproducible research practices
• collaborate with product, business, data science and technology colleagues to understand requirements and translate them into practical analytical solutions
• monitor solution performance, investigate issues and contribute to the continuous improvement of production AI applications
• document methodologies, experiments, technical decisions and results
• communicate technical findings clearly to both technical and non-technical stakeholders
• apply sound judgement when using AI, validating outputs and complying with relevant policies, risk standards and responsible AI principles