Junior Data Scientist

As a Junior Data Scientist, you will support the Data Team in turning raw data into actionable insights that inform credit, product, operations, and business decisions. You will work on real business problems involving customer behavior, lending performance, transaction data, credit risk, and operational analytics. You will also have opportunities to contribute to machine learning and credit-scoring projects under the guidance of senior members of the team. The ideal candidate is curious, analytical, comfortable working with data, and eager to learn.

Core Responsibilities

1. Data Analysis & Reporting

• Write SQL queries to extract, clean, transform and analyze data.
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• Support the preparation of recurring business and portfolio reports.
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• Conduct exploratory data analysis to identify trends, patterns, and anomalies.
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• Support data requests from Credit, Product, Operations, Finance, and other teams.
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• Help validate reported metrics and investigate discrepancies in data.

2. Credit & Portfolio Analytics

• Conduct analysis of loan repayment and customer behavior.
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• Analyze lending metrics such as repayment rates, PAR, DPD, disbursements, collections, and portfolio performance.
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• Perform customer segmentation analysis. 
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• Support monitoring of credit risk indicators and model performance.
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• Contribute to credit scoring improvement ideas.

3. Insights & Reporting

• Respond to data and analysis requests from cross-functional teams (Operations, Product, Finance, Credit).
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• Prepare weekly and monthly reports for internal stakeholders and external lenders, covering portfolio performance and borrower analytics.
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• Support ad-hoc data deep dives to identify performance trends, risks, or operational issues.

4. Model Experimentation Support

• Work closely with senior data scientists to prepare datasets and conduct performance evaluations for machine learning and scorecard prototypes.
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• Participate in feature engineering, validation testing, and performance tracking of classification and credit scoring models.

5. Documentation & Best Practices

• Maintain clear, up-to-date documentation for datasets, metrics definitions, SQL queries, ETL workflows, and dashboards.
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• Contribute to a shared knowledge base including SQL snippets, data dictionaries, and ETL logic to support collaboration and onboarding.
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• Follow and promote data quality, QA, and reproducibility best practices.

Key Performance Indicators (KPIs)

KPI

Target

Request SLA

≥95% of internal data requests delivered within agreed timelines

Dashboard Uptime & Quality

≥99% uptime and accuracy for key operational dashboards

Experimentation Support

Support and analyze at least 2 credit/product experiments per quarter

SQL Query Reusability

Develop reusable templates for at least 3 core reporting use cases per quarter

BI Tool Enhancement

Contribute to at least 1 new dashboard or major enhancement per quarter

Documentation Coverage

100% documentation for core queries, ETLs, and reports owned

Cross-Team Collaboration

Positive feedback from at least 2 cross-functional teams per quarter

Learning & Development

Demonstrate increasing independence in SQL, analytics, and data science tasks 

Data Quality

Identify and document data issues encountered in day-to-day work.