Senior Data Scientist

The Role:

We are looking for an enthusiastic Senior Data Scientist to join our growing team. The hire will be responsible for working in collaboration with other data scientists and engineers across the organization to develop production-quality models for a variety of problems across Razorpay. Some possible problems include : making recommendations to merchants from Razorpay’s suite of products, cost optimization of transactions for merchants, automatic address disambiguation / correction to enable tracking customer purchases using advanced natural language processing techniques.

As part of the DS team @ Razorpay, you’ll work with some of the smartest engineers/architects/data scientists in the industry and have the opportunity to solve complex and critical problems for Razorpay.

Responsibilities:

1. Apply advanced data science, mathematics, and machine learning techniques to solve complex business problems.
2. Collaborate with cross-functional teams to design and deploy data science solutions.
3. Analyze large volumes of data to derive actionable insights.
4. Present findings and recommendations to stakeholders, effectively communicating complex concepts.
5. Identify key metrics, conduct exploratory data analysis, and create executive-level dashboards.
6. Manage multiple projects in a fast-paced environment, ensuring high-quality deliverables.
7. Train and maintain machine learning models, utilizing deep learning frameworks and big data tools.
8. Continuously improve solutions, evaluating their effectiveness and optimizing performance.
9. Deploy data-driven solutions and effectively communicate results to stakeholders.

Mandatory Qualifications:

1. 3-6 years experience working with machine learning in a production environment.
2. Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Operations Research, Statistics, Mathematics, Physics).
3. Strong knowledge of fundamental machine learning techniques, such as regression, classification, clustering, and model evaluation metrics.
4. Proficiency in Python and familiarity with languages like C, C++, or Java.
5. Experience with scripting languages like Perl and command-line Unix is a plus.
6. Experience with deep learning frameworks (TensorFlow, Keras, PyTorch) and big data tools like Spark, and 2-3 years experience in building production-quality machine learning code on platforms like Databricks
7. Experience with AWS / GCP / Microsoft Azure for building production quality ML models and systems
8. Ability to conduct end-to-end ML experimentation, including model experimentation, success reporting, A/B testing, and testing metrics.
9. Excellent communication skills and the ability to keep stakeholders informed of progress and potential blockers.