Data Scientist

Data Analysis & Business Insights :

-Analyse large structured and semi-structured datasets to generate business insights for financial products and customer behaviour.

-Translate business problems into analytical frameworks and data science solutions.</li><li>Perform exploratory data analysis to identify trends, patterns, and opportunities for product growth.

Machine Learning Model Development :

-Design, develop, and validate machine learning models for use cases such as: o Customer propensity models o Cross-sell / up-sell prediction o Customer segmentation o Risk or fraud-related analytics.

-Apply statistical and machine learning techniques such as logistic regression, tree-based models, boosting algorithms, and clustering.

Model Deployment & Lifecycle Management :

-Deploy ML models into production environments.

-Build pipelines for model monitoring, retraining, and performance tracking.

-Maintain and optimize existing models to ensure accuracy and stability.

Collaboration with Business & Product Teams :

- Work closely with product, risk, marketing, and business teams to understand requirements.

-Convert analytical outputs into actionable recommendations.</li><li>Support decision-making through data-driven insights and dashboards.

Advanced Analytics & AI :

- Knowledge or hands-on exposure to Large Language Models (LLMs) and Generative AI.

- Experience in LLM-powered analytics assistants, RAG pipelines, or conversational data interfaces is an advantage.