Data Scientist-FinTech

Job Title: Data Scientist (5+ Years Experience) FinTech

Location: [City / Remote]

Experience: 5+ Years

Industry Preference: FinTech / Banking / NBFC / Lending

Role Overview

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

• 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 modelsCross-sell / up-sell predictionCustomer segmentationRisk or fraud-related analyticsApply 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.

Required Skills

Technical Skills

Strong programming skills in Python or R.Solid knowledge of SQL and working with large datasets.Experience with machine learning frameworks such as Scikit-learn, XGBoost, or similar.Experience in feature engineering, model evaluation, and hyperparameter tuning.Experience deploying models using APIs, batch pipelines, or ML platforms.Analytics Skills

Strong foundation in statistics and predictive modelling.Experience in propensity modelling and customer behaviour analytics.Ability to translate business problems into analytical solutions.Data Tools

Experience with cloud platforms (AWS/GCP/Azure) is preferred.Familiarity with data visualization tools (Power BI, QuickSight, Tableau) is a plus.

Domain Experience

Prior experience in FinTech, Banking, Lending, NBFC, or Financial Services.Understanding of customer lifecycle, lending products, credit analytics, or cross-sell strategies. Job Title: Data Scientist (5+ Years Experience) FinTech

Location: [City / Remote]

Experience: 5+ Years

Industry Preference: FinTech / Banking / NBFC / Lending

Role Overview

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

• 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 modelsCross-sell / up-sell predictionCustomer segmentationRisk or fraud-related analyticsApply 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 act