Senior Quantitative Analyst - Desk Strategy

Key Skills

Analytical and Problem solving, C++, Data cleaning / Data cleansing, Data Manipulation, Data Mining, Effective communication skills, Finance, Linux Operating system, Machine learning techniques, Microsoft-Excel, Python Programming, R Programming, SQL, Statistical modeling, VBA

Job Description

Quantitative Analytics Desk Strategy - New York, NY

As a Barclays QA Desk Strategy team member, you will be responsible for the quantitative models and analytics used in the credit businesses across Markets and Banking. This will involve a wide range of products and activities, including corporate and emerging market bonds, single-name, and index CDS, leveraged loans, hybrid capital securities, credit options, structured notes, bond financing, total return swaps, CDS clearing, banking book loans, and quantitative strategies and risk modeling.

• Salary/ Rate Minimum: $185,000
• Salary/ Rate Maximum: $225,000

The minimum and maximum salary/rate information above include only base salary or base hourly rate. It does not include any another type of compensation or benefits that may be available.

Barclays is one of the world's largest and most respected financial institutions, with 329 years of success, quality and innovation behind us. We've helped millions of individuals and businesses thrive, creating financial and digital solutions that the world now takes for granted. An important and growing presence in the USA, we offer careers providing endless opportunity.

We’re committed to providing a supportive and inclusive culture and environment for you to work in. This environment recognizes and supports ways to balance your personal needs, alongside the professional needs of our business. Providing the opportunity for all our employees, globally to work flexibly empowers each of us to work in a way that suits our lives as well as enabling us to better service our customers’ and clients’ needs. Whether you have family commitments or you’re a carer, or whether you need study time or wish to pursue personal interests, our approach to working flexibly is designed to help you balance your life.

If you would like some flexibility, then please discuss this with the hiring manager.

What will you be doing?

• Building and maintaining data driven models, embed models and business logic in decision making tools, and supporting analytics to drive pricing and risk decisions
• Collaborating with quant colleagues, portfolio managers, syndicate team, risk managers, the Technology Department
• Being responsible for interacting with other quant, risk, and technology teams
• Supporting and ensuring the successfully delivery of models into our analytic systems and libraries
• Expressing complex technical issues and requirements, and working constructively with all stakeholders
• Supporting and ensuring that all activities and duties are carried out in full compliance with regulatory requirements

What we’re looking for:

• Master’s Degree or PhD in Statistics, Econometrics, Computer Science, Financial Engineering, or another technical discipline (Physics, Engineering, Mathematics)
• Excellent coding skills in Python or R
• Experience working with cleaning and manipulating multiple datasets, incomplete data, time series data, etc
• Experience building statistical models, preferably with financial datasets

Skills that will help you in the role:

• In depth understanding of modelling complex credit products and/or a desire to learn about them
• Proficiency in embedding models in user tools with Excel as the front end
• Experience in coding in one or more of the following: VBA, SQL, C++
• Previous experience working in a fast paced and high-profile transformational program including proven stakeholder management skills

Where will you be working?

You will be working at our Americas Headquarters at 745 Seventh Avenue. This 32-story office tower is located in Times Square in the heart of Manhattan and features a cafeteria, fitness center and state-of-the-art LED signage on the facade of the building.