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.
