Summary: Join the Personalization team as the first dedicated Data Analyst to establish analytical foundations and drive data-informed decisions for ML models and product features. Highlights: 1. Shape innovative user experiences that drive business KPIs 2. Pivotal role in establishing analytical foundations and processes 3. Collaborate cross-functionally to influence product roadmap and ML strategy ### **Role Overview** The Personalization team, within the Machine Learning Chapter and Engineering Department, plays a central role in implementing algorithms that utilize personalization signals to optimize for business KPIs like revenue \& conversions. We focus on metrics, arming our search and product discovery products with powerful personalization capabilities that enhance the shopping experience for millions of users and bring value to customers in the way they care the most about. Your work will help shape innovative user experiences that drive business KPIs, interpret user behavior, and drive product and algorithm improvements. As the first dedicated 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. **About Us** Constructor.io powers product search and discovery for some of the largest retailers in the world. We serve billions of requests every week, and you've probably seen our results somewhere and used our product without knowing it. We differentiate ourselves by focusing on metrics over features, and reinventing search and discovery from the ground up as a machine learning challenge with the specific goal of improving metrics like revenue. We're approximately doubling year over year despite the market slow down and have customers in every eCommerce vertical. We're a passionate team of technologists who love solving problems and want to make our customers' and coworkers' lives better. We value empathy, openness, curiosity, continuous improvement, and are excited by metrics that matter. We believe that empowering everyone in a company to do what they think is best can lead to great things. ### **Challenges you will tackle** * Understand Shopper Behavior: Investigate how product changes affect user behavior and conversion metrics. Use SQL, Python, and Spark to uncover usage patterns, anomalies, and opportunities for optimization. * Design \& Validate Metrics: Define new metrics to measure personalization, 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. **Requirements** ### **Requirements** * 3\+ years analyzing complex experiments and extracting actionable insights from large, noisy datasets. Experience with statistical testing and practical experiment design. * Write optimized 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 personalization 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 behavior and business impact. Connect algorithm changes to real\-world customer outcomes. **Benefits** * Paid Time Off * Work From Home * Training \& Development