Data Scientist

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 behavior 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.

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

.