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Strong Tier-1 brand, common Data Scientist title, mid-level (3-5 years), and likely metro location.
Core ML and data skills are transferable, though fintech domain preference increases domain sensitivity.
Explicit 3-5 year requirement plus ML, deployment and Kubernetes experience raises filtering strictness.
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Develop, implement, monitor, and improve machine learning and deep learning models for financial applications including transaction classification, temporal analysis, and risk modeling using structured and unstructured data.
Analyze large datasets applying statistical techniques and leverage advanced ML methods such as SVM, Random Forest, XGBoost, LightGBM, CATBoost, LSTM, RNN, Transformer, and Large Language Models (LLMs).
Communicate technical problems, insights, and roadmaps effectively to business leaders and clients while identifying resource gaps and proposing scalable, creative solutions.
3-5 years of experience in data science and machine learning model development and deployment.
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, Mathematics, or Statistics.
Familiarity with SQL/Databases and relevant technologies such as Python, TensorFlow, Sklearn, Pandas, Kubernetes, Docker, REST APIs, Event Streams.
Experience with financial data (structured/unstructured) and financial risk modeling is a plus but not mandatory.
Strong technical leadership skills with the ability to solve novel problems in data science and the financial industry.
Comfortable working with complex and diverse datasets and advanced analytics techniques including NLP and statistical modeling.
Experienced in communicating technical content clearly to varied stakeholders and capable of designing scalable machine learning solutions integrating best practices in ML and data engineering.