Prefr(Cred) - Data Scientist

About CRED

CRED is a members-only club that rewards individuals for their timely credit card bill payments by providing them with exclusive offers and access to premium experiences. It is a platform that allows credit card users to manage multiple cards along with an analysis of their credit score. Members with a high Experian or CRIF score are eligible for exclusive rewards upon payment of their credit card bills through the app. Among many of the features in the app are CRED's credit card spend tracking and management feature which provided the user with analysis of spend tracking and efficiency of usage of the card.CRED is also equipped with the CRED protect feature which is an AI backed system that keeps track of every single nuance of a credit card payment journey - right from due date reminders, spend patterns and other card usage statistics. Additionally, when a member makes a credit card payment through the app, they are eligible to a variety of rewards of various forms such as access to events, experiences, gift cards and upgrades from brands like Diesel, Cure.Fit, Myntra, Olive Bar & Kitchen among many more. How to apply for a CRED membership? Since CRED is only for a select few, you can apply for a membership by signing up with your full name and a valid Indian mobile number on the app. Once your credit score is checked from credit bureaus like CIBIL, Experian and CRIF, and your score falls above the accepted eligibility score, you will be accepted as a member of CRED. Once you become a member, you will be able to access the curated set of exclusive rewards and privileges that are offered by CRED.

Job Description

Role - Data Scientist

What will you do?

As a Data Scientist at Prefr, you will apply your creative problem-solving and analytical skills to design and deploy high-impact machine learning models across multiple business functions. You will be responsible for:

Design, validate, and productionize advanced machine learning and deep learning models to generate actionable insights for strategic business decisions. Focus areas include credit risk prediction, propensity modeling, fraud detection, collection efficiency improvement, and other finance-related applications.

Optimize and tune machine learning algorithms for performance and scalability, ensuring seamless integration with production pipelines and robustness in real-world environments.

Develop and maintain monitoring frameworks to track model performance over time, detect data or concept drift, and provide timely, actionable feedback for retraining or recalibration as needed.

Analyze and interpret large, complex datasets from distributed databases to generate and provide valuable actionable insights to stakeholders using big data technologies like Scala-Spark/PySpark.

Drive continuous improvement by exploring, researching, and implementing innovative modeling techniques and algorithms.

Stay up-to-date with advancements in ML/AI and proactively apply new techniques to improve model performance or uncover new opportunities.

As part of our long-term vision, contribute to building agentic systems that automate key parts of the data science pipeline from feature engineering and model selection to monitoring and reporting, enabling faster experimentation and decision-making.

You should apply if you

Have 4+ years of hands-on experience (or 2+ years if holding a Master's degree) in data science, machine learning, or analytics roles solving real-world business problems.

Hold a Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.

Are proficient in programming languages like Python or R, and comfortable working with large datasets and distributed computing tools.

Have a strong foundation in machine learning algorithms, statistical modeling techniques, and data-driven decision-making.

Excels at approaching complex problems with a structured mindset, driving data-backed and practical solutions.

Communicate effectively and enjoy collaborating with cross-functional teams, including product, business, and engineering.

Show strong business acumen, you don't just build models, you build solutions that drive measurable impact.

Are passionate about learning. You stay curious about new techniques, tools, and innovations in the AI space, and are excited to apply them to practical business use cases.