Data Science | 2+ years | Fintech

ePaylater

Mumbai

On-site

INR 1,200,000 - 1,800,000

Full time

14 days+

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Job summary

ePayLater is seeking a data-driven analytics professional to strengthen our analytics capabilities and build scalable data solutions to drive growth in fintech.

You will partner with business teams, develop predictive models, and deliver dashboards and insights that support customer acquisition, churn reduction, delinquency control, and profitability.

Qualifications

  • 2+ years in analytics, fintech or related environments.
  • Strong SQL and Python skills with data-driven decision making.
  • Experience with data management, deployment and performance monitoring.
  • Exposure to A/B testing or test-and-learn approaches.

Responsibilities

  • Define and refine the analytics roadmap aligned with business goals.
  • Build and maintain scalable data infrastructure (data warehouses/marts).
  • Develop predictive models and scoring systems for decision making.
  • Explore alternate data sources to boost predictive capabilities.
  • Design experiments with cross-functional teams: acquisition, churn, delinquency, profitability.
  • Deliver automated reporting, dashboards, MIS, and analytical queries.
  • Monitor model and business metrics; recommend optimizations.
  • Ensure data accuracy and reliability across reporting systems.

Skills

SQL
Python
Analytical thinking
Stakeholder mgmt
Experimentation

Tools

Power BI

Job description

ePayLater enables customers to “Buy Now, Pay Later” for purchases made across online and offline platforms. As a fast-growing fintech organization, we are building innovative payment solutions that simplify customer experiences and drive financial inclusion.

We are looking for a highly analytical and data-driven professional who can help strengthen our analytics capabilities, build scalable data solutions, and contribute to business growth through actionable insights and predictive decision-making.

Key Responsibilities
  • Define, manage, and continuously refine the analytics roadmap aligned with business objectives.
  • Build, deploy, and maintain scalable data infrastructure, including data warehouses and data marts.
  • Develop and maintain predictive models and scoring infrastructure to support decision-making systems.
  • Identify opportunities to leverage alternate data sources for improving predictive capabilities.
  • Partner with cross-functional business teams to design and execute experiments focused on:
    • Customer acquisition
    • Churn reduction
    • Delinquency control
    • Profitability improvement
  • Generate actionable insights through automated reporting, dashboards, MIS, and analytical queries.
  • Monitor model and business performance metrics and recommend optimization strategies.
  • Ensure data accuracy, consistency, and reliability across reporting systems.
Requirements
  • 2+ years of hands-on analytics experience in a fintech, financial services, startup, consulting, or related environment.
  • Strong proficiency in SQL and Python.
  • Good understanding of analytical problem-solving frameworks and data-driven decision-making.
  • Hands-on experience with data management, deployment, and performance monitoring frameworks.
  • Exposure to experimentation methodologies such as A/B testing or test-and-learn approaches.
  • Ability to work with large datasets and derive meaningful business insights.
  • Strong communication and stakeholder management skills.
  • Self-driven, detail-oriented, and comfortable working in a fast-paced environment.
Preferred Skills
  • Experience in fintech, lending, BNPL, or risk analytics domains.
  • Familiarity with BI/reporting tools and dashboarding platforms.
  • Knowledge of predictive modeling, customer analytics, and behavioral scoring models.
Why Join Us?
  • Opportunity to work in a rapidly growing fintech ecosystem.
  • Exposure to high-impact business and data-driven decision-making.
  • Collaborative and entrepreneurial work environment.
  • Opportunity to build scalable analytics systems from the ground up.
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