As a Data Analyst on the Personalization team, you will be pivotal in establishing the analytical foundations, tools, and processes for measuring the impact of our ML models and product features. You'll collaborate closely with engineers (who develop the platform and improve ML algorithms) and product managers to define success, uncover insights, and influence roadmap decisions through data.
What you'll do
Your work will help shape innovative user experiences that drive business KPIs, interpret user behaviour, and drive product and algorithm improvements.
- Understand Shopper Behaviour: Investigate how product changes affect user behaviour and conversion metrics. Use SQL, Python, and Spark to uncover usage patterns, anomalies, and opportunities for optimisation.
- Design & Validate Metrics: Define new metrics to measure personalisation, and model performance. Ensure metrics align with user experience and business goals through rigorous validation.
- Build Analytics Infrastructure: Create scalable dashboards and reporting tools for product, engineering, and leadership teams. Develop debugging tools to explain ranking decisions and identify performance issues.
- Drive Data-Informed Decisions: Partner cross-functionally to design experiments, validate hypotheses, and communicate insights that directly influence product roadmap and ML strategy.
What you need
- 3+ years analysing complex experiments and extracting actionable insights from large, noisy datasets. Experience with statistical testing and practical experiment design.
- Write optimised SQL queries for terabyte-scale data extraction and transformation. Proficiency with distributed systems like Spark for large-scale data processing.
- Strong skills in exploratory analysis and building internal tools. Experience with data science libraries and automation.
- Understanding of ML pipelines, training data quality, and ranking/recommendation metrics. Familiarity with search relevance and personalisation concepts.
- Design metrics that accurately reflect model and product performance. Ensure alignment between technical metrics and business outcomes.
- Create compelling dashboards using Tableau, Looker, or custom dashboards in Python. Present complex findings clearly to both technical and executive audiences.
- Influence product and engineering decisions through data storytelling. Collaborate effectively across teams to drive ML and product improvements.
- Deep curiosity about user behaviour and business impact. Connect algorithm changes to real-world customer outcomes.
