Associate, Repo Trader

 

 

 

Requisition ID: 213945

 Salary Range: 145,000.00 - 175,000.00 

Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.

 

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

 

Title: Associate, Repo Trader

 

Global Banking and Markets 
Global Banking and Markets (GBM) is a leading Canadian Capital Markets and Investment Banking business with a growing platform in the US and Latin America, operating globally for over 100 years. Scotiabank’s strong U.S. presence provides our clients an important bridge to this key global market for trade and investment flows across the Americas and the world. 

Global Banking & Markets provides a full range of investment banking, credit and risk management products and services relevant to the financing and strategic development needs of our clients. Our products include debt and equity financing, mergers & acquisitions, corporate banking, institutional equity sales, trading and research, fixed income products, derivatives, energy, foreign exchange and precious & metals. We also cross-sell the full range of wholesale products and services offered by the Scotiabank Group.  

Be part of an innovative, Global Capital Markets and Investment Banking business with a unique geographic footprint that puts capital to work for our clients across industries!  We work together to drive ambition for every future! 

 

Purpose

Based in New York and reporting to Desk Head of Repo desk, the candidate will be responsible for trading the UST repo book across Specials and GC. The candidate will be expected to manage interest rate risk, pricing client activity, providing commentary and managing internal collateral. To succeed in this role, the candidate requires an in-depth knowledge of UST repo markets, short end US bond markets and global central bank policies.  

 

What You’ll Do:

 

• Champions a customer focused culture to deepen client relationships and leverage broader Bank relationships, systems and knowledge.
• Responsible for shaping and delivering the quantitative equity trading strategy for the Americas, collaborating with technology, analysts and senior leaders.
• Works closely with traders to develop data-driven solutions such as algorithmic strategies, trading signals, risk models, portfolio optimization, flow categorization and clustering.  Ultimately combines these strategies into an automated trading process that leverages our ETF market making business.
• Uses highly complex quant models to identify the equity desk’s net exposure, makes systematic trades to hedge against the risk and develops optimization models to unwind inventory positions.
• Works in all aspects of equity portfolio management: data management, research, factor construction, portfolio construction, portfolio monitoring, alpha generation, etc. with an opportunity to work on new datasets and modeling techniques.  
• Conducts research on large order and execution datasets. Builds a back-test trading environment to apply in-depth data analysis, builds/ improves existing quantitative factors, statistical inference and estimation techniques and research ideas for performance enhancements.
• Understand how the Bank’s risk appetite and risk culture should be considered in day-to-day activities and decisions.
• Actively pursues effective and efficient operations of his/her respective areas in accordance with Scotiabank’s Values, its Code of Conduct and the Global Sales Principles, while ensuring the adequacy, adherence to and effectiveness of day-to-day business controls to meet obligations with respect to operational, compliance, AML/ATF/sanctions and conduct risk. 
• Champions a high-performance environment and contributes to an inclusive work environment. 
• Data Analysis in Python/Q developing flow-based alpha signals and strategies.
• Develop model to consistently predict price movement using statistical and machine learning.