Data Scientist

Razorpay · Bengaluru

  • Experience1–5 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Leveljunior
  • Posted3 Sept 2026
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About Razorpay

Razorpay is hiring in Bengaluru in financial services. This role looks for around 1+ years of experience.

Skills

  • machine learning
  • Python
  • NumPy
  • Pandas
  • scikit-learn
  • Matplotlib
  • Seaborn
  • data preprocessing
  • feature engineering
  • model evaluation
  • cross-validation
  • SQL
  • Git
  • MLOps
  • model deployment
  • pipeline automation
  • credit risk
  • fraud detection

The role

A data scientist at a fintech company develops machine learning models for credit scoring, fraud detection, and risk prediction, using Python, scikit-learn, and SQL. The role also applies feature engineering and MLOps to automate model pipelines and monitor production performance.

Full job description

Responsibilities:

Assist in developing and training machine learning models for credit scoring, fraud detection, and risk prediction.

Perform data cleaning, preprocessing, and feature engineering on large datasets.

Support model evaluation, experimentation, and optimization using various ML algorithms and validation techniques.

Build and maintain automated pipelines for data processing, model training, and evaluation.

Monitor model performance, track data drift, and identify production issues.

Collaborate with data scientists, engineers, and product teams to deliver business-focused ML solutions.

Requirements:

1+ years of experience in machine learning or data science projects.

Strong proficiency in Python with hands-on experience in: NumPy, Pandas, Scikit-learn, Matplotlib / Seaborn

Experience in data preprocessing, feature engineering, and handling large datasets.

Good understanding of ML concepts such as model evaluation, cross-validation, overfitting, and feature importance.

Proficiency in SQL for data extraction and analysis.

Familiarity with Git and collaborative development workflows.

Strong analytical and problem-solving skills.

Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field.

Knowledge of fintech, credit risk, or business analytics domains.

Basic understanding of MLOps, model deployment, and pipeline automation.

Passion for continuous learning and exploring emerging technologies in machine learning and AI.