Data Scientist
4 days ago
Gurugram Haryana, HR, India
Incedo Inc.
Remote
Full-time
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We are currently looking to hire experienced
core Credit Risk/Underwriting Data Scientist
who specialize in building and deployment of core
Credit Risk & Underwriting Models
, specifically candidates who sit at the point of the
lending decision
(whether to approve an applicant for a loan, credit card, or mortgage).
Given your hands-on experience working with credit bureau attributes and end-to-end model productionization,
your profile
looks like a strong fit for this team. Key Position Details:
- Role: Data Scientist / Senior Data Scientist – Credit Risk
- Company: Incedo
- Locations: Gurugram / Pune / Bangalore / Hyderabad / Chennai (Hybrid)
- Experience Required: 4-10 years
- Hybrid: 4 Days from office in a week
- Must have hands-on experience with:
- Bureau Attributes / Credit Bureau Data: Data from credit agencies (e.g., Experian, Equifax, TransUnion, CIBIL, CRIF High Mark). The candidate must know how to ingest, clean, and engineer features from credit trade-lines, delinquency history, credit inquiries, and utilization metrics.
- PD (Probability of Default) Modeling: Understands bureau attributes understand what PD is and so. Building statistical/machine learning models to predict the likelihood that a borrower will fail to make payments.
- Productionization / Deployment: End-to-end experience taking an underwriting model from development/validation all the way into a live production scoring pipeline.
- Key Focus Areas:
- End-to-end development and production deployment of Credit Underwriting & Application Scorecards.
- Deep experience with Credit Bureau Data (Experian, Equifax, TransUnion, CIBIL, CRIF, etc.) and feature engineering on bureau attributes.
- Hands-on expertise in Probability of Default (PD) modeling, WoE/IV techniques, and machine learning algorithms (XGBoost, Logistic Regression, etc.). If you are open to exploring new opportunities, I’d love to connect for a quick 10-minute call to discuss the role details and your career goals. Please share your updated resume along with a convenient time to connect, or feel free to reply directly to this email with below details. Name: Total
Experience:
Relevant
Experience:
Current
Location:
Preferred
Location:
Current CTC (Fixed and Variable): Expected CTC: Notice Period: Last working day, if serving notice: Joining Timeline:
your profile
looks like a strong fit for this team. Key Position Details:
- Role: Data Scientist / Senior Data Scientist – Credit Risk
- Company: Incedo
- Locations: Gurugram / Pune / Bangalore / Hyderabad / Chennai (Hybrid)
- Experience Required: 4-10 years
- Hybrid: 4 Days from office in a week
- Must have hands-on experience with:
- Bureau Attributes / Credit Bureau Data: Data from credit agencies (e.g., Experian, Equifax, TransUnion, CIBIL, CRIF High Mark). The candidate must know how to ingest, clean, and engineer features from credit trade-lines, delinquency history, credit inquiries, and utilization metrics.
- PD (Probability of Default) Modeling: Understands bureau attributes understand what PD is and so. Building statistical/machine learning models to predict the likelihood that a borrower will fail to make payments.
- Productionization / Deployment: End-to-end experience taking an underwriting model from development/validation all the way into a live production scoring pipeline.
- Key Focus Areas:
- End-to-end development and production deployment of Credit Underwriting & Application Scorecards.
- Deep experience with Credit Bureau Data (Experian, Equifax, TransUnion, CIBIL, CRIF, etc.) and feature engineering on bureau attributes.
- Hands-on expertise in Probability of Default (PD) modeling, WoE/IV techniques, and machine learning algorithms (XGBoost, Logistic Regression, etc.). If you are open to exploring new opportunities, I’d love to connect for a quick 10-minute call to discuss the role details and your career goals. Please share your updated resume along with a convenient time to connect, or feel free to reply directly to this email with below details. Name: Total
Experience:
Relevant
Experience:
Current
Location:
Preferred
Location:
Current CTC (Fixed and Variable): Expected CTC: Notice Period: Last working day, if serving notice: Joining Timeline: