At a Glance
- Tasks: Lead data analysis to boost customer growth and retention through insights and predictive models.
- Company: Join a dynamic team focused on data-driven decision-making in a remote-friendly environment.
- Benefits: Enjoy flexible hours, private healthcare, generous leave, and unique perks like puppy therapy!
- Why this job: Make a real impact on business strategy while collaborating with diverse teams in a fast-paced setting.
- Qualifications: 5+ years in data science, strong SQL and Python skills, and experience with A/B testing.
- Other info: Work remotely with just one day a month in London; dogs are welcome in the office!
The predicted salary is between 36000 - 60000 Β£ per year.
Location: Remote (1 Day per month in London office)
Department: Data
Reports to: Director of Data
About the Role
Weβre looking for a Lead Data Analyst to join our Growth team and play a key role in maximizing both customer acquisition and retention across the business. Youβll work closely with growth, product, and analytics stakeholders to uncover insights, build predictive models, and drive high-impact decision-making through experimentation and forecasting. This is a high-visibility role where your work will directly influence business strategy, growth initiatives, and long-term customer value. Our data warehouse is hosted in Big Query and we use Power BI as our visualisation tool.
What You'll Do
- Develop and maintain robust LTV (Lifetime Value) forecasting models to inform marketing spend and retention strategies.
- Build demand forecasting models to ensure demand planning aligns with acquisition and retention trends.
- Design, run, and analyse A/B tests and multivariate experiments across acquisition funnels, onboarding flows, and retention programs.
- Collaborate with cross-functional teams to translate business goals into data-driven solutions.
- Communicate insights clearly and effectively to technical and non-technical stakeholders.
- Delve into large-scale datasets using SQL and perform advanced statistical techniques in Python.
- Continuously evaluate and improve existing models and testing frameworks to increase predictive accuracy and business impact.
How Will Your Time Be Spent?
- 30% on Analysis β Mining and analysing customer, growth, marketing, and operations data to uncover insights that inform strategic decisions. This includes building dashboards, running cohort analyses, and identifying trends and anomalies.
- 25% on Data Science & Modelling β Developing predictive models (e.g., churn prediction, CLV forecasting, segmentation models), conducting A/B test analysis, and applying machine learning to solve real business problems. Collaborating with engineers to validate and deploy models.
- 20% on Strategy & Stakeholder Collaboration β Partnering with marketing, product, CX, and leadership to understand data needs, guide decision-making, and translate insights into clear business actions. Identifying opportunities for optimization across customer journey touchpoints.
- 15% on Execution & Tooling β Writing SQL/Python code, automating reports, maintaining data pipelines, and improving data infrastructure in collaboration with data engineers.
- 10% on Experimentation & Innovation β Designing and analyzing experiments (e.g., pricing tests, offer variations), researching new techniques or tools, and staying ahead of industry best practices.
Requirements
What We're Looking For
- 5+ years of experience in data science, ideally in growth, marketing, or product-focused roles.
- Understanding of LTV modeling, forecasting, and experimental design.
- Comprehension of A/B testing methodologies.
- Proficiency in Python for data analysis and modeling (e.g., pandas, scikit-learn, statsmodels).
- Strong SQL skills and experience working with large datasets in modern data environments.
- Experience working with cross-functional growth or marketing teams.
- Competent user of data visualisation tools.
- Comfortable working in fast-paced, agile environments with changing priorities.
- Excellent communication skills with the ability to explain complex topics to non-technical audiences.
Nice to Have
- Experience in e-commerce, subscription services, or other consumer-facing businesses.
- Exposure to tools like DBT or GCP Cloud Platform.
- Understanding of causal inference techniques and uplift modeling.
Benefits
- Private Health Care through Vitality.
- Generous Annual Leave - 28 days + public and bank holidays.
- Flexible Working Hours β We focus on results and trust people to manage their time, whether working from home, while travelling, or in the office!
- Help@Hand β Employee Assistance Programme.
- Royal London Pension Scheme β We offer a workplace pension scheme with one of the UKβs leading providers of group pensions. With an employer contribution of 5% through salary sacrifice!
- Enhanced Maternity / Paternity / Adoption Leave β because time with new family members is important!
- Puppy Therapy β working in partnership with Paws in Work to provide a boost of oxytocin twice a year.
- Generous Learning and development budget β We always want you to keep learning.
- Free breakfast, fruits and snacks β refuel and revitalise with free munchies in the office.
- Working Environment β dogs are welcome!
- Life Assurance β In the event of your death, while employed by us, your chosen beneficiaries will be provided with a tax-free lump sum equivalent of four times your basic salary.
- Gympass β All in one subscription bringing you the largest selection of gyms, studios and apps.
- Electric Vehicle Scheme β Employees sacrifice salary in return for a new electric car, typically saving 30-40% of costs through income and tax and national insurance.
- Give Back Day β An extra day off in the year to volunteer plus a Β£50 contribution to your chosen charity.
- Health Cash Benefit β We offer the bronze package which enables you to claim a certain amount of cashback when you pay for something that is health related, i.e dental.
Lead Data Analyst - Growth & Retention employer: DataAnalystJobs.io
Contact Detail:
DataAnalystJobs.io Recruiting Team
StudySmarter Expert Advice π€«
We think this is how you could land Lead Data Analyst - Growth & Retention
β¨Tip Number 1
Familiarise yourself with LTV (Lifetime Value) modelling and forecasting techniques. Understanding these concepts will not only help you in interviews but also demonstrate your readiness to contribute to our growth strategies from day one.
β¨Tip Number 2
Brush up on your SQL and Python skills, especially in the context of data analysis and modelling. Being able to showcase your proficiency in these areas during discussions will set you apart as a strong candidate.
β¨Tip Number 3
Prepare to discuss your experience with A/B testing and experimental design. We value candidates who can effectively communicate their insights and methodologies, so having concrete examples ready will be beneficial.
β¨Tip Number 4
Network with professionals in the data science and growth fields. Engaging with others in the industry can provide valuable insights and potentially lead to referrals, increasing your chances of landing an interview with us.
We think you need these skills to ace Lead Data Analyst - Growth & Retention
Some tips for your application π«‘
Tailor Your CV: Make sure your CV highlights relevant experience in data analysis, particularly in growth and retention. Emphasise your skills in SQL, Python, and any experience with predictive modelling or A/B testing.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about data analysis and how your previous experiences align with the role. Mention specific projects where you've successfully influenced business strategy through data-driven insights.
Showcase Your Technical Skills: Be explicit about your proficiency in tools like Big Query and Power BI. If you have experience with data visualisation or machine learning, make sure to include examples of how you've applied these skills in past roles.
Prepare for Potential Assessments: Given the technical nature of the role, be ready to demonstrate your analytical skills. This could involve solving a case study or completing a technical task related to data analysis during the interview process.
How to prepare for a job interview at DataAnalystJobs.io
β¨Showcase Your Analytical Skills
Prepare to discuss your experience with data analysis, particularly in growth and retention contexts. Be ready to share specific examples of how you've used data to drive decision-making and improve business outcomes.
β¨Demonstrate Your Technical Proficiency
Familiarise yourself with the tools mentioned in the job description, such as SQL and Python. You might be asked to solve a technical problem or explain your approach to data modelling, so brush up on your coding skills and be prepared to discuss your methodologies.
β¨Communicate Clearly
Since you'll be working with both technical and non-technical stakeholders, practice explaining complex data concepts in simple terms. This will show your ability to bridge the gap between data insights and actionable business strategies.
β¨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about past projects where you designed experiments or built forecasting models, and be ready to discuss the challenges you faced and how you overcame them.