Pitch Yourself

About the role

You solve high-impact problems for millions of shoppers in a small, fast-moving team. The role evolves as you grow, focusing on where your impact matters most. You directly improve product quality and growth for a platform serving contemporary, resale, and luxury shoppers.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Millions of shoppers use a product that connects contemporary, resale, and luxury retail segments through partnerships with 9,600+ retail brands. Data from product usage guides the scanning of more than 350 million products so shoppers locate the right items at optimal prices. Price accuracy and selection quality drive a 50% reduction in return rates as the platform moves toward nine-figure sales growth this year. You shape, improve, and scale solutions for commerce through your analysis and execution. Taking ownership of meaningful outcomes propels the high-trust environment and accelerates impact.

Requirements

Handling ambiguity becomes a routine practice when you focus on impact instead of rigid job descriptions or titles. Initiative, fast learning, and growth in high-expectation settings define how you operate within the team. Effectiveness in a small, lean team that ships at high velocity and operates at startup speed guides your daily contributions.

Practical notes

This role is based in New York City and requires in-person presence. The team is small, high-performing, and operates at startup speed with high ownership. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role operates where consumer AI meets commerce, using agentic approaches to transform shopping experiences. The product functions as an end-to-end destination built to earn user trust over time. Core tools involve AI alignment methods adapted for commerce workflows. The environment emphasizes fast shipping cycles and rapid learning in a high-trust setting.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Phia has raised $43M from Notable Capital, Khosla Ventures, and Kleiner Perkins to build the AI alignment layer for commerce. In under a year, Phia's consumer shopping agent has surpassed one million users and partnered with 6,200+ retail brands, representing billions in annual gross merchandise volume.