Product Analyst, AI Quality

About the role

This position serves as the critical bridge between strategic product objectives and the execution of machine learning systems within the domain of real estate photo editing. The hire will own the end-to-end analysis of AI quality, ensuring that model outputs meet rigorous standards for real-world deployment. You will own the definition of success metrics and the design of evaluations that determine whether AI features deliver value or require redesign. A core responsibility is to translate ambiguous product questions into structured analytical investigations that the ML team can act upon. You will own the maintenance of an exemplar library of outputs that represents best practices and guides future feature development. The role involves coordinating resources for large-scale assessments and tracking regressions to ensure long-term model reliability. You will document reasoning and decisions so clearly that stakeholders across product and engineering can understand and trust the data. Ultimately, this role ensures that data and ML efforts are tightly aligned with business goals around visual media workflows.

Key facts

What you'll do

Datasets are scoped alongside the ML team, with labeling guidelines and data quality standards written to direct acquisition.

An exemplar output library is procured and maintained to support AI-assisted features and kept current.

Resources involved in assessments at scale are coordinated, and regressions are tracked over time.

Requirements are translated into structured evaluation frameworks that measure quality, consistency, and usability of AI output.

Metrics are designed, monitored, and iterated on to reflect evolving product goals and user expectations.

Cross-functional dependencies are surfaced early through proactive coordination with operations and production teams.

Clear specifications and guidelines are authored to communicate expectations to partners and technical collaborators.

Data is interrogated against standards, and reasoning is documented to create an auditable trail for decision making.

Outcomes are synthesized into narratives that explain the business impact of AI quality initiatives to diverse stakeholders.

Continuous improvement cycles are driven by feedback from evaluations, leading to refinements in data pipelines and model behavior.

Requirements

The posting states a pay range of $81000 to $105600.

Experience in product analysis, technical program management, or operational roles spans 1 5 years with data or ML exposure.

Background in real estate photography, commercial photo editing, or visual media workflows is required.

Comfort with evaluating structured data against standards and documenting reasoning is necessary.

Strong written communication creates clear specs and guidelines.

Organization and proactivity enable effective planning cadences.

Familiarity with basic ML and data concepts such as labeling and model evaluation is expected.

Coordination with operations and production teams on large-scale review work occurs routinely.

Practical notes

This role operates remotely within the United States.

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

Evaluating AI output quality forms the core focus for real estate workflows.

Common tools include evaluation frameworks, data labeling processes, and model performance tracking.

Cross-functional collaboration drives successful outcomes in this position.

Clear written communication remains essential for producing specifications and guidelines.

This work enables scalable ML operations and continuous product improvement over time.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works.

Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable.

Questions about the manager's priorities are especially valued.

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.