Lead Data Analyst

Love2shop
Liverpool
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Analyst - Millennial Specialty Insurance

Head of Data Architecture

Head of Data Architecture

Business Intelligence Analyst

Organisation Data Integrity Lead

Head of Data

About the Job

Role: Lead Data Analyst

Hybrid Role: 2/3 days in office/WFH

Office location: Liverpool city centre (L3)


Who We Are❤️

Welcome to Love2shop! We’re a vibrant company that helps people celebrate life’s special moments—at home, work, play, and anywhere else. How do we do it? By offering a fantastic range of gift cards and vouchers that open the door to hundreds of top high street brands and retailers.



We’re big in both consumer and business markets, with over 60% of the UK recognizing our brand. That’s a lot of people loving what we do!


With 55+ years under our belt, we know our stuff. But we’re not just about the past—we’re forward-thinking and progressive. We recently joined the PayPoint family, and we’ve got some exciting developments on the horizon.


As a disability-confident committed company, we’re all about championing equality. We welcome everyone—regardless of disability, age, race, religion, gender identity, or sexual orientation. Everyone gets a shot at success here at Love2shop.❤️


Join Us! ❤️


We’re on the lookout for aLead Data Analystto join us at Love2shop. This role is the perfect fit for someone who likes managing/developing a team but likes to stay 'hands on' by delivering actionable insight for multiple Love2shop B2C and B2B brands.


This is a crucial role for the business, you will lead the build of a fully automated suite of reports to bring insights to the business and support decision making, identify improvement opportunities with existing processes to get them fully automated and system-driven.


Not only that work closely with the business and BI teams to gather requirements for data improvements, and deliver self-serve reporting to reduce manual inputs and errors, and drive greater visibility of the data and trends.




Main Responsibilities ❤️


  • Set customer segmentation, analyse and report on customer behaviours and provide an understanding of customer acquisition, retention and lifetime value to help form strategic sales, marketing, channel and product plans
  • Build reporting and set relevant KPI’s to the appropriate business areas for Commercial Finance
  • Work closely with Marketing and Digital Marketing to produce reporting to give visibility of customer trends across their lifecycle and across all customer touchpoints, to show positive and negative trends of acquisition and retention of customers, and to analyse marketing effectiveness
  • Own the customer, using insights to form customer profiles to aid internal departments plan their strategy and activity
  • Gather retailer and customer insights to inform marketing promotional and strategic opportunities with retailers
  • Proactively have frequent contact with stakeholders to provide actionable insight around marketing channels, customer and business performance and market trends along with response to ad hoc requests
  • Oversee and deliver end-to-end machine learning models to give greater insights and better data, with actionable reporting
  • Track the impact of marketing campaigns, policy changes, and other customer actions to allow for better future decision making


Essentials skills required ❤️



  • Ability to investigate, interpret and translate data into actionable insight
  • Understanding of customer segmentation techniques, customer acquisition and lifetime values, predictive analytics and forecasting
  • Expert of data manipulation and programming in SQL
  • Expert of Data Visualisation tools such as Power BI, Tableau, etc.
  • Experience in marketing campaign analysis
  • Can demonstrate commercial awareness with an understanding of the retail environment
  • Team player with proven leadership skills
  • Excellent presentation skills


Desirable but not essential ❤️



  • Understanding of ETL processes
  • Ability to understand, create and use efficient data models
  • Knowledge of best practices within SQL, to create efficient code, stored procedures and views
  • Good knowledge of python, and the ability to implement Machine Learning and data science techniques
  • Experience with Microsoft Fabric


Benefits if you decide to join us ❤️



  • 25 days’ holiday + bank holidays ️
  • Company pension scheme
  • UK health care cover
  • Discounts with multiple UK retailers ️
  • Fabulous kitchen space with free tea, coffee and snacks ☕
  • Family-friendly leave
  • Community volunteering policy
  • Regular company-wide social events ✨




Ready to join the fun? If you're interested in apermanent role & hybrid working, apply now and join a talented group of people who love what they do.❤️

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.