Senior Data Scientist

Department: Engineering

Location: New York City

Compensation: $100K – $240K

Employment Type: FullTime

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 is building the AI layer that connects consumers and brands, powering a personalized shopping agent that translates real-time consumer intent into trusted product discovery. Launched in April 2025, Phia has surpassed 1 million users in under a year and partnered with 6,200+ retail brands, representing billions in annual gross merchandise volume. Each month, Phia drives millions of dollars in sales for brands and has achieved 11× revenue growth since launch, with partners seeing higher conversion rates, stronger new customer acquisition, increased average order value, and significantly reduced return rates.

Phia’s core experience is an AI shopping agent embedded directly within users’ natural shopping flow, delivering real-time insights that help users make smarter, more confident purchase decisions.

Phia is a small, high-ownership team building at startup speed. If you want to ship at high velocity, solve complex problems in consumer AI and commerce, and have clear ownership from day one, 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.