Data Scientist

About Ganit

Founded in 2017 ,Ganit is a full-stack data and AI company focused on helping businesses make better decisions through data and technology.

We specialize in Data Engineering, Data Science, AI/ML, Generative AI, MLOps, and advanced analytics. We work with clients across industries such as Banking and Financial Services, Retail, CPG, Manufacturing, Healthcare, and Pharmaceuticals.

Our focus is on building scalable, business-driven solutions that help organizations improve decision-making, reduce risks, and drive business growth.

Ganit is also an AWS Advanced Tier Services Partner, strengthening our capabilities in cloud and AI-driven solutions.

We have a presence in India and the US, and offer opportunities to work on diverse projects involving emerging technologies and real-world business challenges.

Responsibilities / Description

Role: Senior Data Analyst / Data Scientist

Experience:

Location:

Employment Type:

Role Overview

Credit Risk, Credit Risk Modelling, and Risk Analytics

Key Responsibilities

• Analyze large datasets to identify trends, patterns, and insights related to credit risk and portfolio performance.
• Develop, validate, and monitor credit risk models for risk assessment and prediction.
• Work on credit risk modelling techniques, including Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), wherever applicable.
• Perform risk analytics, portfolio analysis, customer segmentation, and creditworthiness assessment.
• Build and evaluate statistical and machine learning models to support credit risk management.
• Conduct data cleaning, feature engineering, exploratory data analysis, and model performance evaluation.
• Collaborate with business, risk management, and technical teams to translate business requirements into analytical solutions.
• Prepare reports, dashboards, and presentations to communicate insights and model outcomes to stakeholders.
• Monitor model performance and identify opportunities to improve model accuracy and risk assessment processes.

Required Skills and Qualifications

• 3 - 5 years of relevant experience in Data Analytics, Data Science, Credit Risk, or Risk Analytics.
• Hands-on experience in

  credit risk modelling and credit risk analytics

  .
• Strong proficiency in Python and SQL.
• Experience with statistical analysis, predictive modelling, and machine learning techniques.
• Understanding of credit risk concepts, risk scoring, credit underwriting, and portfolio risk assessment.
• Experience with data visualization tools such as Power BI or Tableau is an advantage.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience in banking, financial services, fintech, or lending domains.
• Knowledge of credit scoring models, Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).
• Familiarity with model validation, model monitoring, and regulatory risk frameworks.
• Experience working with large datasets and translating analytical findings into actionable business insights