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 ModelingData 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.