Senior Data Scientist

As a Senior Data Scientist, you will play a key role in empowering consumers and creating business value by designing, developing, and deploying intelligent solutions across a diverse range of clients including Financial Institutions, FinTechs, SMEs, Large Enterprises, and Government Agencies. You will focus on delivering "Certainty as a Service" through explainable and trustworthy models in domains such as lending, financial decisioning, management, and payments. Key Responsibilities: • Design, develop, and deploy advanced machine learning and deep learning models for production use. • Create consumable metrics, explainability frameworks, and confidence measures for decision-making solutions. • Drive insights from complex and diverse datasets, including structured, unstructured, and time-series data. • Work across Verification Services, Entity Resolution, Attribute & Temporal Analysis, and related platforms. • Perform advanced error analysis, dimensionality reduction, and model interpretability assessments. • Translate business problems into data science solutions in lending, credit scoring, and financial management. • Collaborate with cross-functional teams to ensure successful deployment via Kubernetes, Docker, APIs, and event-driven architecture. • Communicate findings clearly with both technical and non-technical stakeholders. Required Qualifications: • Master’s Degree or higher in Data Science, Computer Science, Information Systems, or a closely related field. A Ph.D. is preferred. • 10+ years of commercial experience in Machine Learning / Deep Learning, including model development and deployment. • Strong experience in Python for data science and software development. Solid foundation in: • Natural Language Understanding (NLU). • Computer Vision. • Statistical Modeling • Data Visualization. • Classical and Advanced ML methods. • Experience in model interpretability and explainability. • Demonstrated ability to solve novel problems in the financial domain. • Strong experience with Kubernetes, Docker, REST APIs, GraphQL, and event streaming. • Excellent written and verbal communication skills. Preferred Qualifications: • Exposure to credit risk modeling, risk evaluation, and financial decision systems. • Experience in Finance or FinTech domains. • Expertise in discrete, differential, deterministic, and probabilistic mathematical modeling. • Advanced skills in Transformer models, Attention Mechanisms, PCA or other dimensionality reduction techniques. • Strong background in Data Architecture, Query Optimization, and ML Ops. • Familiarity with positive attribution techniques, decision science in lending, and attribute analysis.