Quantitative Analytics Manager - Treasury

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Location: 4900 Tiedeman Road, Brooklyn Ohio ABOUT THE JOB (JOB BRIEF) The Quantitative Analytics Manager or Senior Associate (level commensurate with experience) is primarily responsible for using advanced mathematical techniques to develop predictive time-series models related to Pre-Provision Net Revenue (PPNR) for Comprehensive Capital Analysis and Review (CCAR) stress testing and the corporate forecast, Interest-Rate Risk (IRR) for Asset-Liability Management (ALM) and Liquidity Risk. The Quantitative Analytics Manager leverages advanced mathematical knowledge, analysis, partnerships, and business knowledge to provide solutions to predictive and prescriptive questions such as "What will happen next?" and "What will we do?". Projects undertaken by the Senior Quantitative Analytics Associate are often broad in scope across multiple business segments and involve guiding a team and/or project through providing solutions to business problems leveraging statistics, best practices or emerging techniques, and quantitative tools / techniques. Success factors include: Demonstrating leadership through strong communication skills, addressing conflict, coaching others on developing technical skills; managing competing priorities and presenting holistic, thoughtful analyses to answer partners' problem statements; prioritizing multiple projects and managing to tight deadlines; establishing reputation as an effective and collaborative partner; Communicating technical theories, observations, and models to a non-technical audience; Leveraging knowledge of strategy, business, and competition to connect day-to-day work of team to the "bigger picture" and driving efficiency in solution delivery ESSENTIAL JOB FUNCTIONS Create and leverage models, inferential statistics and prescriptive analysis to proactively solve business problems answering the questions "What will happen and what should we do about it?" Often responsible for large, complex problems that have broad implications and are less frequent Recommend solutions based on understanding of the context, connections, and conclusions Reviews deliverables; proactively coaches others on approach and work product Lead and evangelize on best practices of capturing and retaining data Coordinate with data stewards and anticipate needs process/procedures Make continuous improvements to data procedures, including data efficiency Recommend best analysis method for the situation REQUIRED QUALIFICATIONS Master's degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or other quantitative disciplines and at least 5 years of relevant experience; or Bachelor's degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or other quantitative disciplines and at least 6 years of relevant experience DATA LITERACY Understanding of: Best practices for capturing / retaining data Pros / Cons of competing analysis methods Experience leading by: Partnering with others to anticipate and understand needs process/procedures Leading information practices / policies / procedures Setting standards and expectations for data analysis tools and techniques; ensuring compliance with application Promoting increased efficiency of data analysis by advocating clearer data requirements TECHNOLOGY & TECHNIQUES Advanced Microsoft Office Suite SQL/NoSQL Relationship data structure Selecting and retrieving data including unstructured data retrieval, archival, and ETL Databases Advanced Python/R/SAS: Databases Efficient coding Can build strong code controls and translate code into high-level commentary Understanding of and ability to leverage: Cloud-based computing Distributed computing MODEL BUILDING & MAINTENANCE Ability to: Establish standards and best practices; forecast future modeling tools / techniques Identify, employ, and evangelize emerging techniques from industry / research Coach others on data modeling methods / techniques Facilitate sessions for complex data models Assess and understand risks; contingency plans Communicate observations to senior executives Translate technical observations to a non-technical audience EXPECTED COMPETENCIES Leadership: Demonstrated leadership; may have direct reports; Assumes accountability for their work; Sought out for advice; Proactively coaches and guides the work of others; Manages the integration of activities typically within own team; Demonstrates executive presence; Offers an opinion, contributes to the conversation Partnering / Influencing: Demonstrated ability to engage and partner at mid to senior leadership levels; Established reputation and track record as an effective and collaborative partner; Coaches and develops relationship building skills in others; Demonstrates managerial courage; willing to dissent from others; leverages organizational and professional savvy and persuasive skills to influence others Business

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