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.

Must have skills

Model Development Experience : 4+ years of hands-on experience in building, validating, and deploying machine learning models in production environments (within fintech, lending, or related domains is a plus)

Programming Expertise : Strong proficiency in Python, with practical experience using libraries like pandas, NumPy, scikit-learn, and frameworks such as XGBoost, LightGBM, or similar.

Machine Learning & Statistics : Solid understanding and practical implementation experience of ML algorithms (e.g., logistic regression, random forest, gradient boosting, clustering) and statistical techniques (e.g., hypothesis testing, feature selection).

Data Handling : Strong experience in handling large datasets and data pipelines; familiarity with SQL, distributed data environments, or PySpark is a plus.

Problem Solving & Business Thinking : Ability to break down complex business problems into data science approaches and deliver actionable solutions with measurable impact.

Collaboration & Communication : Proven ability to work in cross-functional teams alongside data engineers, business analysts, and product managers. Experience in articulating findings and recommendations clearly to non-technical stakeholders or leadership.