Lead Data Analyst, Petlab

• 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

Desirable

• Reports to: Director of Data
• We’re 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
• You’ll 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
• 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
• 30% on Analysis – 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 – 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 – 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 – Writing SQL/Python code, automating reports, maintaining data pipelines, and improving data infrastructure in collaboration with data engineers
• 10% on Experimentation & Innovation – Designing and analyzing experiments (e.g., pricing tests, offer variations), researching new techniques or tools, and staying ahead of industry best practices