Senior Market Data Analyst - VP | London, UK

Morgan McKinley
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Market Data Analyst - Dry Bulk Freight

Senior Data Analyst - Trading Data Specialist EMEA (F/M/D)

Business Data Analyst

Senior Data Analyst

Supply Chain Data Analyst CGT

Senior Data Engineer

About the job

The Market Data Analytics Team is part of the Traded Risk Management (TRM) department, supporting a wide area of businesses within Financial Markets and Group Treasury. The team is responsible for all aspects of market data used by the TRM department including market risk, product control, and counterparty credit risk.

Client is looking for a Senior Market Data Analyst/ Business Analyst to join the Market Data Analytics Team (MDAT) supporting a team focused on the management of market data sourcing, transformations, models, and proxies, as well as the development of dashboard and data analytics and reporting tools, forming a market data centre of excellence within TRM. We are looking for a candidate who has proven hands-on experience in managing projects, analysing, and visualising large sets of market data, as well as a background in Market Risk and some derivatives pricing knowledge.

Main Duties and Responsibilities of Role:

  • Developing and maintaining derived market data and proxy models for multiple asset classes
  • Conducting analysis on large data sets, processes, and technology systems
  • Coordinating and implementing market data used in market risk-related internal initiatives as well as upcoming regulatory changes and strategic market data programmes.
  • Being a point of contact to resolve market data queries from our stakeholders including Trading, Risk and Model Validation
  • Collaborating with the global team across the full market data lifecycle including requirements, architecture, implementation, testing, and release management
  • Working closely with the TRM teams to understand and deliver on their market data requirements.
  • Co-ordinating agile projects with IT developers and system support teams to develop and maintain high market data quality standards on behalf of TRM.
  • Working closely with Model Validation to close out market data related findings.
  • Daily market data validation & reporting, defined by the Market Data Replacement & Proxy Policy


Candidate Profile

Qualification/Education

Essential:

  • Graduate (at least UK 2:1 degree) in a quantitative subject (i.e. Mathematics, Computer science, Physics, Chemistry).
  • Economics/Finance degrees considered with a quantitative focus.


Desirable:

  • Professional qualification (e.g., PRM, FRM, CQF)


Experience/Knowledge

Essential:

  • Statistical and mathematical background
  • Experience in dealing with large market data sets and associated technologies.
  • Experience writing in Python to build solutions particularly with large datasets
  • SQL experience
  • Good quantitative level of experience with several asset classes, their risks and pricing
  • Strong level of understanding of quantification of market risk metrics (VaR, HVaR, IRC, etc.) and market risk capital requirements


Desirable:

  • Experience with and keen interest in financial markets relevant policies, guidelines and regulations.
  • Understanding of regulatory frameworks as applied internally.
  • Closing out model validation and audit findings
  • Azure DevOps and Git version control
  • Experience with systems such as Summit, Murex, Bloomberg, Superset DAP, Reuters, ActivePivot is also a plus.


Personal Competencies

Essential:

  • Strong quantitative problem-solving skills (this will be tested at interview)
  • Skills which ideally included managing direct reports in a previous role
  • Strong communication skills: Position involves regular interaction with colleagues in varying degrees of seniority and understanding of new rules and processes. Candidate should be able to adapt communications to target audiences ranging from senior executives to junior colleagues involved/impacted by new rules and processes.
  • Self-starter, proactive, results-oriented and flexible, can adjust quickly to new circumstances.
  • Must be capable of working and delivering using initiative and time very efficiently under tight timelines.
  • Team player with a mindset of empowering others
  • Delivery focused.
  • Hands-on approach with good analytical skills
  • Good verbal and written communication skills

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Jobs Careers

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Jobs

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.