Data Scientist

Durus Consulting; Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Durus Consulting in Chennai is seeking a hands-on ML specialist to design, develop and validate credit risk scorecards using ML, AI and statistical techniques on financial and alternate data.

You will own the end-to-end model lifecycle from development to deployment, monitor performance, and collaborate with engineering to integrate scoring logic into production systems. Strong SQL and Python skills are essential to translate business requirements into analytical solutions.

Qualifications

• Hands-on ML model implementation and deployment experience.
• Strong SQL and Python proficiency.
• Experience with model validation and monitoring is preferred.

Responsibilities

• Lead the design, development and validation of credit risk scorecard models using ML, AI, and other statistical techniques on Financial and Alternate Data.
• Perform advanced EDA, feature engineering, and data preparation on large datasets.
• Translate business and risk requirements into analytical solutions and support integration into production systems (AUC, KS, Gini).
• Own end-to-end model lifecycle: development, validation, deployment and ongoing monitoring.
• Collaborate with engineering teams to integrate models into production systems (APIs, batch scoring, real-time decisions).
• Develop monitoring dashboards and reports to track model performance and portfolio health.
• Produce technical documentation, validation and model reports for stakeholders.
• Provide guidance and knowledge transfer across teams.

Skills

Machine Learning

SQL

Python

Job description

Hands-on experience : Machine Learning model implementation and deployment, SQL, Python.

Key Responsibilities

• Lead the design, development and validation of credit risk scorecard models using ML, AI, and other statistical techniques, using Financial and Alternate Data.
• Perform advanced exploratory data analysis (EDA), feature engineering, and data preparation on large, complex datasets
• Translate business and risk requirements into analytical solutions and support their integration into production systems.(e.g., AUC, KS, Gini)
• Own end-to-end model lifecycle: development, validation, deployment and ongoing monitoring
• Partner with engineering teams to integrate models into production systems (APIs, batch scoring, real-time decisioning) and collaborate with application teams to ensure robust and scalable implementation of scoring logic
• Develop monitoring frameworks, dashboards, and reports to track model performance, drift and portfolio health
• Produce high-quality technical documentation, validation reports, model reports and stakeholder presentations
• Provide technical guidance, best practices and task allocation to team members and support knowledge transfer across teams.