Job Description
We are looking for an experienced Data Scientist with strong analytical capabilities and hands-on experience in building, deploying, and maintaining machine learning models in a FinTech environment. The role requires translating business problems into data-driven solutions, particularly in areas such as customer propensity modelling, risk analytics, and customer behaviour analysis.
The candidate should be comfortable working with large datasets, collaborating with business teams, and operationalizing models in production environments.
Key Responsibilities
1. 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.
Perform exploratory data analysis to identify trends, patterns, and opportunities for product growth.
2. Machine Learning Model Development
Design, develop, and validate machine learning models for use cases such as
Customer propensity models
Cross-sell / up-sell prediction
Customer segmentation
Risk or fraud-related analytics
Apply statistical and machine learning techniques such as logistic regression, tree-based models, boosting algorithms, and clustering.
3. 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.
4. Collaboration with Business Product Teams
Work closely with product, risk, marketing, and business teams to understand requirements.
Convert analytical outputs into actionable recommendations.
Support decision-making through data-driven insights and dashboards.
5. Advanced Analytics AI (Good to Have)
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.
