The Role

We seek a talented Data Scientist to collaborate with our team. The ideal candidate will work alongside other data scientists and engineers across the organization to develop high-quality models for various challenges within Razorpay.

Some potential areas of focus include developing recommendations for merchants from Razorpay’s suite of products, optimizing transaction costs for merchants, and enhancing address disambiguation/correction using advanced natural language processing techniques.

As part of the DS team at Razorpay, you’ll have the opportunity to work with some of the industry’s top engineers/architects/data scientists and tackle complex, critical problems for Razorpay.

You must possess the right attitude, and we guarantee a fun and growth-oriented environment.

Key Responsibilities:

• Apply data science and machine learning principles to solve business problems.
• Collaborate with cross-functional teams to build and deploy data science solutions.
• Analyze large datasets to generate actionable insights.
• Present findings and recommendations to stakeholders.
• Identify key metrics, conduct exploratory data analysis, and create executive dashboards.
• Support multiple projects in a fast-paced environment.
• Train and maintain machine learning models.
• Continuously improve solutions and evaluate their effectiveness.
• Deploy data-driven solutions and communicate results effectively.

Mandatory Qualifications:

• 1-2 years of experience in ML and building ML models.
• Bachelor’s or Master’s degree in a quantitative field such as Computer Science, Operations Research, Statistics, Mathematics, or Physics.
• Familiarity with basic machine learning techniques: regression, classification, clustering, model metrics, and performance (AUC, ROC, precision, recall).
• Proficiency in coding in Python and knowledge of at least one additional language from C, C++, Java.
• Experience with scripting languages such as Perl or command-line Unix.
• Interest in deep learning frameworks like TensorFlow, Keras, PyTorch, and big data tools like Spark and Databricks/AWS/GCP/Microsoft Azure.
• Ability to experiment with models, report success, and perform A/B testing.
• Effective communication skills and ability to keep stakeholders informed of progress/blockers.