About the Role
We are looking for a driven and detail-oriented Data Scientist to join our team. The ideal candidate will bring strong hands-on experience in Machine Learning and model validation. This role requires someone who can independently drive analytical projects, communicate findings clearly to stakeholders, and continuously learn and adapt in a fast-paced environment.
Key Responsibilities
- Design, build, and validate machine learning models to solve business problems within the BFSI / credit card domain (e.g., fraud detection, risk scoring, customer behavior analytics)
- Perform rigorous model validation, including performance testing, stability checks, and documentation to meet internal and regulatory standards
- Translate business problems into structured data science approaches, and communicate technical findings to both technical and non-technical stakeholders
- Apply strong problem-solving skills to troubleshoot data issues, model performance gaps, and business edge cases
- Work independently with minimal supervision, managing multiple priorities and deadlines with strong organizational discipline
- Stay current with evolving ML/AI techniques and proactively bring new ideas and approaches to the team
Required Qualifications
- Proven experience in Machine Learning - building, tuning, and deploying models
- Experience with model validation practices (performance metrics, stability/drift checks, documentation)
- Strong communication skills, able to explain technical concepts to varied audiences
- Strong problem-solving ability with a structured, analytical approach
- Strong organizational skills and ability to manage work independently
- A quick learner with genuine enthusiasm for picking up new tools, techniques, and domain knowledge
Preferred (Good to Have)
- Experience with AI techniques/tools beyond traditional ML (e.g., LLMs, generative AI applications)
- Background or working understanding of the BFSI / Credit Card business
- Familiarity with cloud-based ML platforms (e.g., AWS SageMaker) and explainability techniques (e.g., SHAP)
- Exposure to regulatory/compliance considerations in financial model development