Machine Learning Vice President

Responsibilities

• Lead the design, development, and delivery of end-to-end machine learning solutions to address strategic business opportunities in Wealth Management, delivering measurable business outcomes.
• Leverage AI/ML modeling and algorithms to deliver use cases supporting the growth plan across Client advisor and product strategy.
• Build modeling solutions at speed and scale to solve complex business problems across large client and advisor populations.
• Investigate, design, and create experimental prototypes focused on specific business domains and verticals.
• Analyze large, complex data sets to quantitatively reveal underlying patterns, correlations, trends, and growth opportunities.
• Strive to develop and experiment with state-of-the-art algorithms, including advanced machine learning, deep learning, recommender systems, and emerging AI approaches.
• Support and enhance existing models to ensure improved performance, stability, scalability, and business impact.
• Set up and conduct large-scale experiments, including A/B tests, to test hypotheses and drive business growth.
• Validate machine learning models in collaboration with validation teams to ensure accuracy, reliability, explainability, and compliance with model governance standards.
• Deploy machine learning models in production environments in collaboration with MLOps and technology teams, and monitor performance over time.
• Participate in and lead code reviews, modeling reviews, and technical design discussions to raise engineering and modeling standards across the team.
• Build, grow, and strengthen partnerships with business stakeholders, Marketing, Digital, Product, Risk, Legal, Compliance, Technology, and other cross-functional partners.
• Create executive-ready presentations and analytical narratives to effectively communicate modeling results, business implications, and strategic recommendations to senior stakeholders.
• Mentor junior data scientists and contribute to the development of team best practices, reusable modeling assets, and scalable AI/ML frameworks.

Qualifications

• Master’s degree or Ph.D. preferred in an analytical or technical field such as Computer Science, Engineering, Applied Mathematics, Physics, Statistics, Operations Research, or an equivalent quantitative discipline.
• Minimum of 8 years of professional experience in data science, machine learning, AI, advanced analytics, or a related quantitative field.
• Advanced knowledge of statistical and machine learning methods, particularly in modeling, classification, regression, recommender systems, clustering, deep learning, and experimental design.
• Demonstrated hands-on experience building models at speed and scale to solve complex commercial or business problems.
• Experience conceiving, implementing, deploying, and continually improving machine learning projects in production or production-like environments.
• Minimum of 8 years of experience programming in SQL, Python, and/or R.
• Proficiency in autonomously conducting applied ML research with commercial applications and translating business problems into scalable modeling solutions.
• Strong familiarity with higher-level trends in artificial intelligence, generative AI, LLMs, and open-source AI/ML platforms.
• Experience working with AWS, Azure, Google Cloud, or similar cloud platforms.
• Experience with code versioning systems such as GitHub or Bitbucket, and experiment tracking systems such as MLflow or equivalent.
• Proficiency with computer science fundamentals, including object-oriented design, data structures, and algorithmic design.
• Strategic thinker and influencer with demonstrated leadership acumen, problem-solving skills, and ability to drive outcomes across cross-functional teams.
• Strong communication skills with experience presenting technical concepts, modeling results, and business recommendations to senior business stakeholders.
• Familiarity with visualization techniques and software to communicate analytical insights effectively.
• Proficiency in English

Preferred

• Experience with Cloud or Big Data technologies such as Azure, AWS, Google Coud, Hadoop, or an equivalent
• Familiarity with Deep Learning frameworks (PyTorch, Tensorflow, PyTorch – Geometric, or equivalent).
• Experience with Graphical Neural Networks, Reinforcement Learning, LLMs, Transformer based Models, or Recommender Systems is a plus.
• Track record of publishing in peer-reviewed scientific journals