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Tier-1 brand, popular Data Scientist title, mid-level range, and metro location increase candidate competition.
Core ML and deployment skills transfer across industries, though fintech/transactional experience is a beneficial preference.
Explicit 3-5 years plus required ML model development and deployment experience makes filters moderately strict.
Manipulate large datasets and apply advanced statistical and machine learning techniques to draw insights and solve analytical problems.
Design, implement, measure, validate, monitor, and improve machine learning models for financial applications, including transaction classification, temporal analysis, and risk modeling from structured and unstructured data.
Present technical problems and findings clearly to business leaders and clients, and propose innovative solutions new to the company and financial industry.
3-5 years of experience in data science or machine learning model development and deployment.
Bachelor’s or Master’s Degree in Computer Science, Information Technology, Engineering, Mathematics, or Statistics.
Experience with financial transactional structured and unstructured data, transaction classification, risk evaluation, or credit risk modeling is a plus.
Preferred technical skills include SQL/Database, Python, TensorFlow, Sklearn, Pandas, Kubernetes, Containers, Docker, REST APIs, and Event Streams.
Strong technical leadership with the ability to innovate and address novel problems in data science and finance.
Skilled in NLP, statistical modeling, visualization, and advanced data science techniques, especially leveraging text data and annotations.
Capable of effectively communicating complex technical analyses and roadmaps to diverse stakeholders including business leaders and clients.