Overview

As a Senior Data Scientist at Phia, you will drive the models and measurement systems behind our core feed and recommendation experiences. You’ll own high-impact problems across ranking, personalization, and optimization – from metric design and experimentation to modeling and iteration. You will partner closely with Product and Engineering to translate user behavior into scalable models and rigorous experiments that improve engagement, retention, and long-term ecosystem health. This role has direct influence on product strategy, growth, and user experience as we scale.

About Phia

Phia has raised $43M from Notable Capital, Khosla Ventures, and Kleiner Perkins, with backing from founders and operators like Vlad Tenev (Robinhood), Mellody Hobson (Ariel Investments), Naomi Gleit (Meta), and Mati Staniszewski (ElevenLabs), plus a roster of cultural leaders, to build the AI alignment layer for commerce. In just over a year, Phia's consumer shopping agent has surpassed 1.5 million users and partnered with 9,600+ retail brands across contemporary, resale, and luxury, representing billions in annual gross merchandise volume. We scan more than 350 million products to help shoppers find the right pieces at the best price, cutting return rates by 50%, and we're on pace for nine-figure sales growth this year.

In an era where AI vertical agents are reshaping every industry, commerce is on the verge of a complete transformation. Phia is reinventing shopping from a fragmented, impersonal experience into one that feels intelligent, trusted, and built around each user's intent. This foundation of trust is our wedge to become the end-to-end shopping destination for the next generation of buyers.

Phia is a lean, high-ownership team building at startup speed. If you want to ship at high velocity and solve complex problems in consumer AI and commerce, this is the place to do it.

What You Own

• Own performance of feed ranking and recommendation systems, optimizing for engagement, retention, and long-term user value.
  Define and evolve core product metrics, balancing short-term lift with ecosystem health (diversity, fatigue, creator impact).

• Design and analyze A/B tests to evaluate model and product changes with strong causal rigor.

• Build and iterate on personalization, ranking, and propensity models using large-scale behavioral data.

• Develop robust offline evaluation frameworks that predict online impact.

• Partner closely with Product and Engineering to turn ambiguous problems into measurable experiments and scalable solutions.

• Elevate experimentation standards and modeling best practices across the team.

Qualifications

• 3+ years of experience in data science or applied ML within consumer tech, ideally with feed or recommendation systems.

• Strong proficiency in Python and SQL; experience with ML frameworks such as XGBoost, LightGBM, PyTorch, or TensorFlow.

• Experience working with large-scale behavioral data and high-dimensional, noisy datasets.

• Strong foundation in experimentation, A/B testing, and causal inference.

• Experience building and evaluating ranking, recommendation, or personalization models.

• Excellent cross-functional communication and product intuition.

• Bachelor’s degree in a quantitative field or equivalent practical experience.