Digital Data Analyst

INOVERSE GROUPE
Bath
2 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst/ Consultant (SuccessFactors, MS Excel, Mapping)

Senior Data Analyst

Senior Data Analyst

HR Data Analyst - Kempston

Senior Data Analyst

Data Analyst

Our client, based in Bath, is seeking a talented Digital Analyst with Data Analyst experience to join their team on a permanent basis. This role is pivotal in providing analysis, statistical, and insight solutions across a range of clients. You will play a key role in developing the client portfolio, identifying client needs, and recommending the right solutions. If you enjoy contributing to the overall success of a company, and want the opportunity to grow, this could be the right fit for you.

Key Responsibilities:

  1. Deliver data-driven reporting and insights, especially in digital marketing and multi-touch attribution.
  2. Troubleshoot web analytics issues and manage analytics platforms.
  3. Improve data capture mechanisms and marketing analytics infrastructure.
  4. Leverage SQL for in-depth analysis and optimise data structures.
  5. Offer consultancy on measurement and attribution strategies.
  6. Produce analysis addressing client briefs and track customer journeys.
  7. Research analytical opportunities and innovate new methodologies.

Essential Requirements:

  1. Experience with SQL and web analytics data.
  2. Proficiency in Excel and Office tools.
  3. Experience with GA4, Adobe Analytics, and tag management systems.
  4. Dashboard management using Looker Studio, PowerBI, and Tableau.
  5. Knowledge of R, Python, JavaScript, HTML, and CSS.
  6. Strong attention to detail and communication skills.
  7. 2-3 years of experience in an analytical field.

If you feel like you'd be a good fit, and are looking for your next step, please do get in touch! My client offers a collaborative and supportive environment alongside a generous salary of up to £60K dependant on experience.

#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.

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!

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.

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.