Quant Analytics Lead Assoc
About The Position
Under some supervision, the Lead Quantitative Analytics Associate uses statistics, advanced mathematical techniques, and/or computer science to develop and validate predictive and machine-learning models for specific business needs. The role applies advanced mathematical knowledge and analysis to answer predictive and prescriptive questions, such as what may happen next and what actions to take. Projects may be large in scope and involve self-directed data analysis and model building in response to business-partner problem statements. Success in the role includes completing assigned work effectively and on time with manager and/or peer guidance; applying analytical programming, data literacy, and model-development skills; communicating insights and data to peers; and developing autonomy and problem-solving ability. The position is eligible for a base salary of $71,000 to $125,000 annually, plus eligibility for incentive compensation that may include production, commission, and/or discretionary incentives. The employer prioritizes in-office presence while offering flexible options where work can be performed effectively in a mobile environment.
Requirements
- Bachelor’s degree or equivalent in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or another quantitative discipline.
- At least 2 years of relevant experience, or 1 year with a master’s degree or Ph.D.
- Ability to create data structures and transformations and identify and capture information needed for business needs or analysis.
- Knowledge of data controls, hypothesis testing, root-cause analysis, and implementation considerations.
- Advanced Microsoft Office Suite skills.
- Knowledge of SQL/NoSQL, relational data structures, databases, and selecting and retrieving data, including unstructured-data retrieval, archival, and ETL.
- Advanced Python, R, or SAS skills, including efficient coding, establishing strong code controls, and translating code into high-level commentary.
- Understanding of cloud-based and distributed computing and ability to leverage them.
- Understanding of model use, requirements, implementation needs, Model Risk Management processes and foundations, model deterioration and health testing, machine-learning concepts, statistical measurements, and advanced data techniques for modeling frameworks.
- Ability to identify the preferred modeling approach for a problem statement.
- Solid writing and communication skills, including the ability to organize and present findings and recommendations and communicate a data-supported message.
- Critical-thinking and problem-solving skills, including analyzing moderately complex problems, recommending appropriate solutions, explaining why results occur, and beginning to understand cross-functional impacts.
- Ability to understand business-partner strategy and banking at a high level, ask relevant questions, and understand upstream and downstream impacts.
- Developing relationship-building, interpersonal, and influencing skills, typically at the peer or working-group level.
Responsibilities
- Conduct quantitative analysis, including hypothesis testing and root-cause analysis, on large data sets with increasing autonomy.
- Identify information needed for analysis or to answer business questions, and create data structures and transformations for group use.
- Use statistical analysis and machine learning to develop and maintain models, while anticipating implementation considerations and ensuring models address the appropriate business need.
- Apply critical thinking to select an appropriate approach for each problem statement.
- Anticipate business needs and continuously improve models and processes.
- Produce and identify information through statistical analysis.
- Explain model insights effectively to peers and the analytics community.
- Present and organize findings and recommendations, using data to communicate a clear and compelling message.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
