Are you motivated by the opportunity to translate complex marketing investments into measurable commercial impact for one of the world’s leading Global Banking franchises? Join our AIM team as a Data Science Lead, where you will be the senior analytics partner to Global Banking Marketing leadership, advancing the Measure What Matters (MWM) framework and delivering high-impact, decision-grade analytics.

As the Data Science Lead within the Corporate and Investment Banking Marketing Analytics, Insights & Mar Tech (AIM) organization, you will spearhead advanced marketing measurement and analytics for the Global Banking business. You will be responsible for designing and implementing return on investment frameworks, valuation models, and decision analytics to quantify the business impact of marketing investments and guide the strategy of senior stakeholders. In this highly visible Vice President-track role, you will influence executive decision-making, shape marketing measurement strategy, and collaborate closely with senior marketers, finance, and business stakeholders. You will integrate rigorous data science with compelling storytelling to quantify marketing’s contribution to pipeline, revenue, and long-term brand value across Global Banking segments and products.

Job Responsibilities:

• Lead measurement and evaluation of marketing performance across Global Banking campaigns, channels, and client segments, advancing ROI coverage and optimization insights within the MWM framework.

• Develop analytical valuation models that quantify the long-term commercial impact of marketing investments, incorporating brand measurement inputs and business growth drivers.

• Apply attribution, causal inference, and experimental design techniques to assess how marketing influences client engagement, pipeline progression, and revenue outcomes.

• Design forecasting and scenario models that support target setting, investment planning, and business case development for Global Banking marketing initiatives.

• Deliver executive-ready analyses, presentations, and scorecards that translate complex statistical findings into clear, actionable insights for senior marketing and business stakeholders.

• Serve as the senior analytics advisor to Global Banking Marketing, shaping measurement approaches, prioritizing analytics initiatives, and guiding data-driven decision-making across programs.

• Advance AIM capabilities in areas such as marketing mix modeling, multi-touch attribution, AI/ML-driven insights, and integrated ROI frameworks aligned to enterprise measurement standards.

Required Qualifications, Capabilities, and Skills:

• Bachelor’s or Master’s degree in Data Science, Statistics, Economics, Finance, or related quantitative field.
• 6+ years’ experience in data science, marketing analytics, or commercial analytics roles.
• Demonstrated ability to translate complex business questions into rigorous quantitative analyses and actionable insights.
• Strong proficiency in SQL and Python (or similar analytical languages); experience working with large, multi-source datasets.
• Advanced Excel and PowerPoint skills with ability to produce executive-level materials.
• Experience with predictive modeling, marketing performance measurement, or customer behavior analytics.
• Exceptional communication and stakeholder-management skills, with ability to influence senior non-technical audiences.
• Experience supporting Global Banking, Corporate Banking, or B2B financial services businesses.
• Experience with marketing and CRM platforms such as Salesforce, Marketo, Adobe, or similar Mar Tech ecosystems.
• Experience with attribution modeling, causal inference, marketing mix modeling, or ROI frameworks.

Preferred Qualifications, Capabilities, and Skills:

• Experience supporting Global Banking, Corporate Banking, or B2B financial services businesses.
• Familiarity with Global Banking products, client segments, and go-to-market models.
• Experience with marketing and CRM platforms such as Salesforce, Marketo, Adobe, or similar Mar Tech ecosystems.
• Experience with attribution modeling, causal inference, marketing mix modeling, or ROI frameworks.
• Exposure to brand measurement, long-term marketing impact modeling, or valuation analytics.