Staff Analyst

For over 30 years, Angi has powered the future of the home services industry, creating an environment where homeowners and pros benefit from more jobs done well. For homeowners, our platform is a reliable way to find skilled pros. For pros, we're a reliable business partner who helps them find the winnable work they want, when they want. For employees, we're an amazing place to call home. We can't wait to welcome you. Angi at a glance: - Founded in 1995 as Angie’s List and rebranded in 2021 - Global company with 9 brands in 8 countries and employees worldwide - Homeowners have turned to us for 300 million home projects and counting About the Role We are seeking an experienced and data-driven Staff Analyst to join our Sales Analytics team as a senior individual contributor. This role will focus on optimizing the performance of our Sales Organization by building and maintaining the analytical systems that power how we score, prioritize, allocate, and contact prospects. You will work closely with sales leadership, operations leaders, and data engineering teams to design predictive models, evaluate dial strategies, and deliver actionable insights that directly drive revenue growth. As a key contributor, you will own the end-to-end analytics behind prospect scoring, territory alignment, lead allocation, and sales rep performance — translating complex data into decisions that move the business forward. What you'll do - Continuously improve our prospect lead scoring models, including conversion probability estimation, expected revenue modeling, and composite lead score generation ensuring scores accurately reflect prospect quality and are actionable for sales reps. - Analyze and optimize dial strategy including when to dial, how frequently to attempt contact, and resting strategy (when to temporarily pause outreach on a prospect) — using profitability and conversion data to maximize return on rep time and phone capacity. - Design and evaluate territory alignment and lead a

Department: Data and Machine Learning; Team: Data and Machine Learning