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Tier-1 brand, mid-level ML role, metro location and broad skillset drive high applicant competition.
Core ML skills are transferable, but wealth-management domain and model validation needs raise domain specificity.
Explicit 3-5 years ML requirement, domain-specific skills and model validation controls increase filtering strictness.
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Develop and deploy machine learning models for Wealth Management business verticals to generate predictive analytics and business intelligence.
Monitor, recalibrate, and troubleshoot models in production in collaboration with ML Ops and engineering teams.
Communicate model outcomes and performance to US-based team members, leadership, and business stakeholders through clear presentations and reports.
Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, or equivalent quantitative field (Master's preferred).
3-5 years of machine learning experience, with at least 3 years in the ML domain; financial services industry experience preferred.
Proficiency in programming languages such as Python, C++, or related languages and experience with version control (GitHub, Bitbucket) and experiment tracking (ML Flow).
Strong knowledge of ML algorithms (classification, regression, clustering, deep learning) and computer science fundamentals (OOP, data structures, algorithms).
Experienced in collaborating with multi-disciplinary teams including Risk, Legal, Compliance, and business partners in financial services contexts.
Capable of independent problem solving, model validation, and deployment support, with accountability for maintaining model performance.
Comfortable presenting complex analytical results clearly to varied audiences including leadership and cross-functional stakeholders.