Credit Risk Data Scientist

DCM Infotech Limited; India; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

DCM Infotech Limited is seeking a highly analytical Credit Risk Data Scientist to join the Risk Analytics team. You will develop and deploy statistical and ML models to assess and manage credit risk, influencing lending decisions and risk strategies.

The role requires 8+ years' experience in credit risk modeling, proficiency in Python, R, SQL, SAS, Spark, and familiarity with Basel/IFRS 9/CCAR. Collaboration with cross-functional teams and communication of complex metrics to both technical and

Qualifications

• 8+ years of experience in credit risk modeling or analytics.

Responsibilities

• Develop, validate, and monitor credit risk models (e.g., PD, LGD, EAD) using statistical and machine learning techniques.
• Analyze large datasets to identify patterns, trends, and risk indicators.
• Support the design and implementation of risk scoring models for underwriting, collections, and portfolio monitoring.
• Work closely with cross-functional teams (Credit, Product, Finance, Compliance) to align risk models with business objectives.
• Use tools like Python, R, SQL, SAS, and Spark for data analysis and model development.
• Perform model validation, backtesting, and sensitivity analysis to ensure robustness and regulatory compliance.
• Prepare detailed documentation for models to meet internal governance and regulatory standards (e.g., SR 11-7, Basel, IFRS 9, CCAR).
• Monitor performance of deployed models and recommend enhancements as needed.
• Communicate complex analytical concepts and risk metrics to both technical and non-technical stakeholders.

Skills

Strong analytical thinking

Problem-solving

Communication skills

Detail-oriented

Agile working

Education

Master's or PhD in Statistics, Data Science, Economics, Finance, Mathematics, or related field

Tools

Python

R

SQL

SAS

Spark

Hadoop

Tableau

Power BI

Job description

Job Summary:

We are seeking a highly analytical and detail-oriented Credit Risk Data Scientist to join our Risk Analytics team. In this role, you will be responsible for developing and deploying statistical and machine learning models to assess and manage credit risk across our portfolio. Your work will directly influence lending decisions, risk strategies, and overall business performance.

Key Responsibilities:

• Develop, validate, and monitor credit risk models (e.g., PD, LGD, EAD) using statistical and machine learning techniques.
• Analyze large datasets to identify patterns, trends, and risk indicators.
• Support the design and implementation of risk scoring models for underwriting, collections, and portfolio monitoring.
• Work closely with cross-functional teams (Credit, Product, Finance, Compliance) to align risk models with business objectives.
• Use tools like Python, R, SQL, SAS, and Spark for data analysis and model development.
• Perform model validation, backtesting, and sensitivity analysis to ensure robustness and regulatory compliance.
• Prepare detailed documentation for models to meet internal governance and regulatory standards (e.g., SR 11-7, Basel, IFRS 9, CCAR).
• Monitor performance of deployed models and recommend enhancements as needed.
• Communicate complex analytical concepts and risk metrics to both technical and non-technical stakeholders.

Qualifications:

• Required:
• Master’s or PhD in Statistics, Data Science, Economics, Finance, Mathematics, or related field.
• 8+ years of experience in credit risk modeling or analytics.
• Proficiency in Python, R, SQL, and statistical modeling tools.
• Experience with machine learning algorithms and large-scale data processing.
• Strong understanding of credit lifecycle, risk metrics, and financial products (e.g., personal loans, credit cards, mortgages).
• Preferred:
• Experience with regulatory requirements such as Basel II/III, IFRS 9, CCAR, SR 11-7.
• Background in consumer or commercial lending.
• Experience with cloud platforms (AWS, GCP, Azure) and big data tools like Hadoop or Spark.
• Knowledge of BI tools (e.g., Tableau, Power BI) for dashboarding and visualization.

Soft Skills:

• Strong analytical thinking and problem-solving skills.
• Ability to communicate insights clearly to non-technical stakeholders.
• Detail-oriented with a high standard of data integrity and accuracy.
• Comfortable working in a fast-paced, agile environment.