Data Scientist

1 week ago

Mumbai Metropolitan Region, India InCred Financial Services Full time ₹ 15,00,000 - ₹ 20,00,000 per year

Job Title: Chief Manager/ AVP - Analytics - Personal Loan (Self-Employed Segment)

Department: Analytics

About the Role:

We are looking for a Chief Manager/ AVP - Analytics to join our Personal Loan – Self-Employed segment team. This role involves developing acquisition scorecards and credit risk models using diverse data sources such as credit bureau, banking, GST, and alternate data. The ideal candidate will also be responsible for shaping credit policy, optimizing the customer journey, and ensuring effective risk management through model validation and portfolio monitoring.

Key Responsibilities:

  • Develop acquisition scorecards - Full-Stack development of ML/Regression Models for Score Cards catering to Personal Loans (PL) for self-employed customers or Unsecured Business Loans (UBL) for MSMEs using multiple data sources.
  • Analyze and interpret GST data, banking transactions, credit bureau reports, and alternate data to enhance risk assessment.
  • Design and implement credit policies and customer journeys tailored to the self-employed segment.
  • Conduct post-implementation validation, monitoring, and performance tracking of risk models.
  • Perform portfolio analytics to identify trends, mitigate risks, and optimize underwriting strategies.
  • Collaborate with cross-functional teams including data science, product, and risk management to drive credit decisioning strategies.
  • Utilize big data tools and advanced analytics techniques to improve credit risk assessment models.

Experience Required:

  • Overall 5 to 8 years of experience in business / risk analytics in a lending company
  • Minimum 2 years of experience in developing acquisition scorecards for PL (self-employed customers) or UBL (MSMEs).
  • Strong understanding of GST data and its application in credit risk analytics.
  • Experience in portfolio management, risk model validation, and credit decisioning strategies.

Skills & Competencies:

  • Technical Skills:
  • Credit Risk Modeling, Data Modeling, and Portfolio Analytics
  • Python, SQL, Advanced Excel, and Data Visualization
  • Data Science / Big Data Analytics and exposure to machine learning techniques
  • Analytical Thinking & Problem-Solving Ability
  • Strong Oral and Written Communication Skills
  • Ability to work with large datasets and extract meaningful insights

Qualification:

  • B.Tech/M.Tech/B.E. in a quantitative field such as Computer Science, Mathematics, Statistics, or Engineering.
  • Certifications in Data Analytics, Credit Risk, or Machine Learning will be an added advantage.

Why Join Us?

  • Opportunity to work in a fast-growing credit risk team with a focus on innovation and data-driven decision-making.
  • Exposure to cutting-edge risk modeling techniques and big data applications.
  • A collaborative work environment with opportunities for career growth and skill development.

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