Quant Analyst- Senior Associate
On-siteNew York City, New York, United States or New York, United States
Job Summary
Deliver effective quantitative problem solving and analytical research for key pricing initiatives while conducting extensive analysis of pricing functionality, settings, and customer behavior to improve strategies and policies. Lead, mentor, and develop a high-performing team of analytics professionals, promoting scientific rigor and ethical AI practices. Partner with cross-functional teams, including Finance and Marketing, to drive impactful results through hypothesis testing, A/B testing, and multivariate experiments that measure the effectiveness of pricing strategies. Design and implement scalable data analytics processes to optimize business outcomes using advanced statistical techniques and machine learning frameworks. Own strategic pricing initiatives, including evaluating effectiveness and performing profit & loss analysis to quantify statistical and practical significance.
Required Qualifications
- Graduate or post-graduate degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Finance, Economics, Data Analytics, or Machine Learning
- 3+ years of hands-on analytics experience in banking strategic analytics
- Python
- SQL
- visualization tool including Tableau & Alteryx
- machine learning frameworks
- Strong statistical and econometric foundation
- hands-on experience in pricing analytics
- elasticity estimation
- A/B testing
- causal inference
- experimentation frameworks
- Solid skills in advanced analytics
- SAS
- R language
- Excellent communication skills to translate and explain complex models with clear reason codes, influencing cross-functional stakeholders and senior leadership
- Exposure to enterprise AI enablement, LLM-assisted workflows, or analytics transformation programs
- Ability to evaluate opportunities to apply AI, GenAI, or intelligent automation to improve investigative analysis, control documentation, operating procedures, knowledge retrieval, issue summarization, and workflow efficiency
- Familiarity with supervised learning
- anomaly detection
- semi-supervised learning
- clustering
- feature stores
- calibration/threshold optimization
- imbalanced learning
- pricing sensitivity evaluation
Desired Qualifications
- Proficient in big data ETL processes for structured and unstructured databases
- Professional experience with AWS
- Spark/EMR
- ChatGPT
- Confluence
- Snowflake
- People leadership: recruiting, coaching, performance management, and fostering an inclusive, high-accountability culture
- Good understanding of IT processes and databases, with ability to work directly with data owners and custodians
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