Market Data Analyst

S4 Market Data
London
1 year ago
Applications closed

Related Jobs

View all jobs

Market Data Analyst Graduate

Market Data Analyst Graduate: Insights & Impact

Data Analyst

Senior Python Engineer & Trading Data Analyst (Hybrid)

Senior Data Analyst – London Market Insurance Data Flows

Senior Data Analyst - London Market Insurance Data Flows

Summary:PLEASE NOTE - This is NOT a technical role for a Data Analyst, Data Scientist or someone with an IT background. Candidates MUST have experience in the Market Data realm and be able to administer Market Data contracts. Please read the description before you applyThe Market Data Analyst at S4 Market Data will oversee client projects and be responsible for the overall service delivery of our managed services with respective clients. This position will manage market data service inquiries and projects from clients as well as manage a market data administrator within the projects to ensure administrative tasks are being completed in an accurate and timely manner. The ideal candidate will have market data vendor management and administrative experience; sourcing and negotiating contracts, managing procurement/sourcing requests throughout the spend life cycle, speaking with internal business units and stakeholders (legal, finance, IT, etc.) to procure goods/services for our clients.  The candidate needs to be located in the US, this is a fully remote position. Responsibilities: Handle day-to-day demand management or vendor management and administrative inquiries from internal business units, including but not limited to; data/sourcing requests, contract negotiation, entitlement administration, exchange reporting, moves/adds/changes requests, inventory management, procurement/legal approval, expense allocation, invoices reconciliation, and spend reporting. Interact with the client’s various internal stakeholders and business units; technology, legal, accounting/finance, human resources, and investment managers. Oversee the inventory management process of leavers/joiners, ensure current inventory is accurate and up-to-date. Oversee the reconciliation invoices and validation of monthly allocations/expenses. Conduct monthly/quarterly exchange reporting and ensure exchange policies and data compliance across the client’s end-users and applications.  Administer their datafeeds (EMRS, DACS, Etc.) Review spend and enact cost savings and avoidance initiatives. Provide respective business units with an overview for their costs; understand their products/services and respond to any inquires as needed. Maintain reports on costs and identify ways to consolidate spend. Conducts regular internal team meetings to report on client SLA’s and to ensure all client service deliverables are being met and completed. Qualifications: Bachelor’s degree in MIS, Business, or related degree and 3-5 years of relevant experience in financial services or market data. Relevant work experience in consulting is preferred. Experience working with Market Data vendors such as Bloomberg, FactSet, Exchanges (NYSE, ICE, etc.). Knowledge of FITS and MDSL inventory systems is preferred. Excellent communication and project management skills and experience in working closely with internal client business units and senior stakeholders. An entrepreneurial and self-regulating mind-set. Display a high level of time management skills to manage multiple and elaborate requests simultaneously. Have high energy and be a self-starter with the ability to work independently and as part of a team. Powered by JazzHR

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.