Sr. Analyst - Data Scientist

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