Lead Data Analyst

Elsevier
London
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Analyst

Lead Reporting and Data Analyst

Senior Data Analyst

Consumer Data Manager

Data Analyst

Data Admin Specialist / Data Analyst

Lead Data AnalystAre you enthusiastic about leading high-impact analytics initiatives in technology infrastructure?Do you enjoy uncovering actionable insights from complex data?About Team:The Analytics and Insights team in the Technology Infrastructure and Operations function develops high-quality management reporting about Elsevier’s technology infrastructure and operational performance. The team works with operations and infrastructure colleagues, senior management and teams across technology and the wider business to deliver robust insights about Elsevier’s technology estate. The team also leads data governance initiatives across TIO to build robust data management practices across the breadth of our infrastructure and operational data.About Role:You will develop and maintain reporting on Elsevier’s vast multi-cloud infrastructure estate and operational performance for senior stakeholders across the business. Engaging with stakeholders across Technology, you will gather analytics requirements and translate these into compelling dashboards and reports on how we best manage our AWS resources, software assets and other areas of operational performance and compliance.You will build advanced data models for reporting, support the development of data pipelines and streamline data integration for analytics and reporting. You will work alongside other data analysts, data engineers, data architects and infrastructure architects in building reporting pipelines and implementing data quality standards and processes.Key Responsibilities:Building and automating ETL pipelines using DBT and Python, and data integration leveraging AWS services such as Lambda, S3, AthenaDesigning and implementing dimensional data models for analytics and reportingCreating Tableau dashboards, reports and data visualizations which provide clear and actionable insights for operations teams and senior stakeholdersAnalysing large operational datasets with a focus on data integrity and accuracyLeading analytics projects independently, taking ownership of initiatives and delivering insights and analytical solutions supporting strategic data initiativesCollaborating with business stakeholders and cross-functional project teams to establish reporting requirementsManaging analytics reports across the full analytics lifecycle including discovery, iterative development, testing, deployment, maintenance and end user supportMentoring and coaching other team members on ETL and data modellingRequirements:Significant experience in a lead role in data analytics, business intelligence or analytics engineeringExperience with DBT, SQL, Snowflake / other relational databases and dimensional data modellingExperience with Python for data analysis, ETL and automationExperience working with large and complex data sets, data profiling and cleansingDashboard development and data visualisation experience using Tableau, presenting data insights clearly and persuasivelyExperience with AWS, in particular, Lambda, S3, Athena, or equivalent cloud technologiesExperience with GitStrong written and verbal communication skills and experience engaging effectively with technical and non-technical stakeholders at all levelsDemonstrate curiosity and a structured and analytical approach to problem-solvingAttention to detail with a keen eye for effective dashboard design, data quality and accuracyWhy Join Us?Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.Work in a way that works for you

Check all associated application documentation thoroughly before clicking on the apply button at the bottom of this description.

We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.Working for youWe know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:Annual Profit Share BonusComprehensive Pension PlanGenerous vacation entitlement and option for sabbatical leaveMaternity, Paternity, Adoption and Family Care leaveFlexible working hoursInternal communities and networksVarious employee discountsRecruitment introduction rewardEmployee Assistance Program (Global)Annual EventAbout the BusinessA global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world’s grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.

#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.