Senior Data Scientist

As a Data Scientist , you will have the opportunity to manipulate large data sets, integrate diverse data sources, data types and data structures into solutions under management guidance. You will build experience developing analytical approaches to meet business requirements, which involves translating requests into use cases, test cases, preparation of training data sets and iterative algorithm development.

What You Will Do

• End-to-End Modeling: Design, develop, and deploy high-performance supervised and unsupervised machine learning models, including linear regression, gradient boosting, and complex classification systems.
• Credit Risk Innovation: Build and refine regulatory-grade Credit Risk models, specifically focusing on Application/Behavioral Scorecards and Loss Given Default (LGD) frameworks.
• Big Data Wrangling: Architect complex data pipelines using PySpark or SQL to clean, aggregate, and engineer features from terabytes of structured and unstructured credit bureau data.
• Gen AI Integration: Implement Generative AI solutions, including LLM fine-tuning and Neural Networks, to automate insight generation and develop Agentic AI workflows for internal analytics.
• A dvanced Analytics: Deliver holistic insights by moving beyond descriptive statistics into prescriptive and inferential analytics to solve complex business problems for BFSI clients.
• Cloud Deployment: Utilize cloud platforms (GCP/AWS/Azure) to scale model training and manage the full ML lifecycle (ModelOps).
• Stakeholder Collaboration: Translate complex mathematical outputs into business-speak for executive leadership and external banking partners to influence strategic decision-making.

• Masters or Bachelors degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics, or Data Science).
• 3 to 6 years of proven experience as a Data Scientist or Analyst, specifically within the Financial Services or Fintech domain.
• Demonstrated experience building Credit Risk Scorecards or Basel III/IFRS 9 models (PD/LGD/EAD).
• Proficiency in Python Pyspark and advanced SQL for data extraction and manipulation. Exposure towards BI tools for data graphical presentation.
• Hands-on experience with Big Data frameworks, specifically Hadoop, Spark, or Hive , to manage large-scale datasets.
• Practical experience implementing Scikit-Learn, XGBoost, or LightGBM in a production environment.
• Minimum 1 year of experience working within a cloud environment (AWS, Azure, or Google Cloud Platform).
• Documented experience in performing inferential statistics and hypothesis testing to validate model performance.

• Advanced knowledge of the credit lifecycle, including experience building models within a regulatory framework.
• Understanding of Generative AI architectures and hands-on exposure to Agentic AI use cases (e.g., autonomous data agents, automated report generation, or intelligent feature engineering workflows).
• A track record of leading multiple projects through the Real-Time Model Development Journeyfrom initial data discovery and hypothesis testing to production deployment and post-launch monitoring.
• Demonstrated experience in Cloud Operations (AWS/GCP/Azure) specifically for scaling analytical workloads, managing data lakes, and optimizing model inference costs.
• Expert-level hands-on coding skills in Python and PySpark for distributed computing, combined with complex SQL optimization and the ability to visualize insights using BI tools.

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