Lead Data Analyst

Love2shop
Liverpool
11 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Analyst

Senior Data Analytics Lead — Strategy & BI

Data Analyst – Insights Leader for Student Life (Flexible)

Fraud Strategy Lead - Data Analytics (Hybrid)

Lead Data Engineer

Statistician

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.❤️

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.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.