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Tier-1 brand, metro location, mid-level ML role, and broad skillset requirements increase candidate competition.
Core ML skills transfer across industries, but finance/wealth-management domain experience and controls increase sensitivity.
Explicit 3–5 years requirement plus mandatory ML domain experience and programming raises screening rigor.
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Build, deploy, and monitor machine learning models to generate predictive analytics and business insights for Wealth Management (WM) business verticals.
Collaborate with model risk, validation, ML Ops, engineering, and business partners to ensure models meet operational and compliance standards.
Communicate model results and impact through clear reporting and presentations to US-based team members and leadership.
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Physics, or equivalent quantitative field.
Minimum 3 years of machine learning experience (overall 3-5 years), preferably in financial services.
Proficiency in at least one programming language such as Python or C++ and experience with version control (GitHub, Bitbucket) and experiment tracking systems (ML Flow).
Experience with theoretical and applied machine learning algorithms including classification, regression, recommender systems, clustering, and deep learning.
Experience working independently on machine learning projects with accountability for delivering solutions with minimal direction.
Strong oral and written communication skills, including ability to present complex technical information to diverse audiences including client-facing roles (2+ years preferred).
Familiarity with Cloud or Big Data technologies (Azure, AWS, Google Cloud, Hadoop) and deep learning frameworks (PyTorch, Tensorflow, Py-Geometric) is a plus.