Relationship Data Analyst

Carter Murray
London
5 months ago
Applications closed

Related Jobs

View all jobs

PIM Data Analyst

Campaign Data Analyst (PEGA, Adobe or Unica)

Senior Data Analyst

Market Intelligence Officer and Data Analyst

Global Foreign Exchange (FX) Services Sales - Sales Data Analyst

Machine Learning Engineer - Personalisation

This Relationship Data Analyst sits within a leading city law firm and will be crucial in leveraging client relationship data to drive the firm's growth and strategic initiatives. The successful candidate will play a key role in the implementation of CRM systems by developing processes, reports, and insights to optimize data utilisation. This collaborative position involves working closely with various departments to ensure the best possible use of client data, from initial intake to ongoing relationship management and reporting.


Within this Relationship Data Analyst role, you will be responsible for:


Data Management and Analysis:

  • Understand and leverage client relationship data to identify strengths and weaknesses.
  • Cleanse, enrich, and maintain data integrity across systems.
  • Design, implement, and monitor data governance protocols.
  • Create insightful reports and visualizations using tools like PowerBI.
  • Ensure data compliance with GDPR and other regulations.


CRM Implementation and Optimization:

  • Support the implementation of CRM systems (Upper Sigma, Introhive, Concep).
  • Configure and optimize CRM systems to meet business needs.
  • Train and support users on CRM system usage.
  • Identify and implement process improvements to enhance CRM effectiveness.


Relationship Management:

  • Analyse client relationships to identify opportunities for growth.
  • Collaborate with BD&M and other teams to improve relationship management strategies.
  • Support the development and execution of relationship-building initiatives.


The ideal candidate for this Relationship Data Analyst role will have:


  • Strong analytical and problem-solving skills.
  • Experience with CRM systems (preferably Salesforce).
  • Proficiency in data analysis tools (e.g., PowerBI, SQL).
  • Understanding of data governance and compliance.
  • Excellent communication and interpersonal skills.
  • Ability to work independently and as part of a team.

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.