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

• Masters 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