Primary Responsibilities:
Lead the design and development of machine learning models across use cases such as fraud detection, customer behavior modeling, document intelligence (OCR/Doc AI), and financial forecasting.
Partner with senior leadership and stakeholders to identify opportunities, define analytical approaches, and translate business needs into data-driven solutions.
Architect and oversee end-to-end production ML systems, including data pipelines, feature stores, model deployment, and monitoring of model performance and drift.
Provide technical leadership, mentorship, and guidance to junior and mid-level data scientists; review work to ensure quality, accuracy, and best practices.
Communicate complex analytical findings to both technical and non-technical audiences; influence decision-making through clear storytelling and recommendations.
Establish and maintain best practices in modeling, documentation, model governance, and performance monitoring to ensure scalability and compliance.
Design and evaluate experiments (e.g., A/B testing, back testing) to measure model impact on business outcomes such as fraud loss reduction, customer retention, and revenue growth.
Drive innovation by researching and applying new tools, techniques, and methodologies in data science and analytics.
Collaborate cross-functionally with data engineering, IT, and business teams to ensure alignment and successful implementation of solutions.
Design and implement NLP, computer vision, and hybrid rule-based/ML systems for document extraction, classification, and text analytics use cases.
Performs other related duties and projects as assigned.