Back
Tier-1 brand, metro location, mid-level ML role, and broad skill requirements increase applicant competition.
Core ML and NLP skills transfer across industries, though fintech transaction risk experience increases domain specificity.
Explicit 5-7 years plus mandatory ML deployment, NLP and infra experience creates strict filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead planning and execution of data science and machine learning projects for financial applications such as transaction classification, temporal analysis, and risk modeling.
Design, implement, and monitor machine learning models using structured and unstructured financial data to improve performance and solve novel challenges.
Communicate technical findings and roadmaps effectively to internal business leaders and external clients, while proposing scalable, creative solutions.
5-7 years of experience in data science or machine learning model development and deployment.
Proficient in machine learning, NLP, statistical modeling, and advanced data science techniques with experience on financial transactional structured/unstructured data preferred.
Experience with relevant technologies such as Python, TensorFlow, Sklearn, Pandas; SQL/database experience preferred.
Bachelor's or Master's degree in Computer Science, IT, Engineering, Mathematics, or Statistics; M.S preferred.
Technical leader comfortable working with financial data and solving problems new to the company and financial industry.
Experience with deployment technologies such as Kubernetes, Docker, REST APIs, and event streaming to build scalable delivery mechanisms.
Able to translate complex technical concepts into clear communications for diverse stakeholders including clients and business leaders.