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Tier-1 brand, mid-level ML role, metro Mumbai, and generalist requirements drive high candidate competition.
ML modeling and deployment skills transfer well, though financial transaction experience increases domain sensitivity.
Explicit 5-7 years and specialized ML/deployment experience increases screening rigor.
Lead planning and execution of data science and machine learning projects focused on financial applications like transaction classification and risk modeling.
Design, implement, deploy, and optimize machine learning models using structured and unstructured financial data; ensure model performance monitoring and improvements.
Communicate complex technical findings and solutions clearly to business leaders and clients, propose innovative and scalable data science solutions addressing new challenges in finance.
5-7 years of experience in data science or machine learning model development and deployment.
Bachelor’s or Master’s degree in Computer Science, IT, Engineering, Mathematics, or Statistics; Master’s preferred.
Experience with machine learning frameworks and tools such as TensorFlow, Python, Scikit-learn, Pandas, SQL/Databases, and container technologies like Docker, Kubernetes.
Exposure to financial transactional data, transaction classification, risk evaluation, or credit risk modeling preferred but not explicitly mandatory.
Experienced technical leader with a strong background in financial data science, comfortable handling both structured and unstructured data including NLP and statistical modeling.
Able to own end-to-end machine learning lifecycle in a production environment, leveraging modern tools and infrastructure for scalable delivery.
Effective communicator who can translate complex data science topics to diverse stakeholders and develop innovative solutions new to the company and financial industry.