Strategy & Analytics for Business Growth Associate
On-site · New York City, New York, United States
Job Summary
Strategy & Analytics for Business Growth Associate on the Global Banking Intelligence & Growth Strategy team. Partner with senior business leaders to identify revenue opportunities across M&A, capital markets, FX, payments, and corporate banking; develop market and product insights from internal data, external deal activity, and sector dynamics; build market sizing, TAM, wallet, and share-of-wallet models; design banker coverage, prioritization, and territory strategies; create analytical workflows and dashboards to guide decision-making; translate analyses into concise, executive-ready recommendations for across-banking and product domains; requires 3+ years in data-driven strategy and advanced analytics; bachelor's degree; skills include SQL, Python, Alteryx, Tableau, Qlik; AI/LLM techniques; strong stakeholder engagement.
Required Qualifications
- Bachelor’s degree in computer science, Statistics, Engineering or related fields
- 3+ years of experience with a leading management consulting firm, top-tier bank, or private equity firm with a focus on data-driven strategy and advanced analytics development
- Demonstrated expertise in revenue-driven analytics, market sizing, TAM and wallet modeling, and capital markets data analysis
- Strong analytical and problem-solving skills, including hands-on experience with large datasets, financial statements, public filings, and AI/LLM tools for insight generation
- Demonstrated experience in building data models (SQL, Python, Alteryx) and visualizations (Tableau, Qlik)
- Exceptional ability to translate complex analyses into clear, executive-level insights and actionable recommendations
- Proven track record of partnering with and influencing senior stakeholders across banking and product organizations
- Experience in financial services with exposure to capital markets, corporate banking, or payments.
- Proficient understanding and application of fundamental AI and ML techniques (e.g., NLP, time series, supervised learning, LLM)
- Bachelor’s degree in computer science, Statistics, Engineering or related fields
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