Data Scientist

Job Description : Responsibilities : - Work with stakeholders across the organization to identify opportunities for leveraging internal and external data to drive business solutions. - Develop a Data Science use case roadmap for problem areas and capabilities. - Act as a data strategist, identifying and integrating new datasets to enhance product capabilities. - Develop end-to-end Credit Risk scorecards/models (application, behavior, collections) using techniques such as linear/logistic regression, random forests, boosting/bagging trees, and dimensionality reduction. Loan Pricing, Loss Forecasting, and Cross-Sell model development. - Mine and analyze data from company databases to drive product optimization and improvement. - Enhance data collection procedures and ensure data integrity through processing, cleansing, and verification. - Conduct data exploration to identify relationships and patterns, and develop data visualizations. - Utilize state-of-the-art data mining methods to select features and build optimized classifiers using machine learning techniques. - Refine and deepen understanding of algorithmic and inferential aspects of statistical analysis. - Evaluate new algorithms and develop intuition about their applicability. - Build training pipelines for production environments and develop plans for continuous model iteration and refinement. - Provide input for design, quality assurance, and implementation of models in online environments. - Determine infrastructure requirements and manage infrastructure for model deployment. - Lead debugging of data pipelines and model behavior in production Develop dashboards to track and communicate model impact. Requirements : - We're looking for someone with 2 5 years of experience manipulating data sets and building statistical models, with a Bachelor's/Master's/PhD degree in Statistics, Mathematics, Computer Science, or another quantitative field, from any of the top-tier colleges. - Strong problem-solving skills with an emphasis on product development. - Excellent written and verbal communication skills for coordinating across teams. - Good applied statistics skills include distributions, statistical testing, and regression. - Good scripting and programming skills in Python and SQL. - Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, artificial neural networks, and their real-world advantages or drawbacks. Knowledge of deep learning techniques is a plus. - Experience with common data science toolkits such as R, NumPy, MatLab, Pandas, Scikit-learn, TensorFlow, Keras, etc. - Experience with data visualisation tools such as D3.js, GGplot. - 3 years of relevant experience within credit, risk, collections management, ideally at a high-growth technology and/or financial services company. (Application Scorecard, Behaviour Scorecard, Collection Score Card, Loan Pricing, Loss Forecasting, Cross-Sell Model) - Experience with NoSQL databases such as MongoDB, Cassandra, and HBase is desired. - Experience with distributed data/computing tools like Map/Reduce, Hadoop, Hive, and Spark is a big plus. (ref:hirist.tech)

Location: bangalore, IN

Posted Date: 4/6/2025