AI Data Strategist

About the role

In this senior individual contributor position, you own the data strategy that allows operations and research teams to convert model failures into measurable improvements. You define how robot episodes are labeled and categorized so that every collection run captures signal that is consistent and actionable for the models. You translate real-world constraints and deployment anomalies into data decisions that directly shape how models are trained and evaluated. You build an organization-wide, real-time view of the data lifecycle to surface issues before they corrupt model behavior. Success in this role is judged by how your strategy turns noisy failures into durable improvements across the product stack. You work closely with ML researchers, operations, annotation teams, and engineering to align data practices with business goals. Your portfolio of past analyses and system designs will often matter more than formal degrees in hiring decisions. You ensure that data collection efforts prioritize quality and relevance over sheer volume.

Key facts

What you'll do

Prioritize data collection initiatives so the team focuses on gathering data that meaningfully improves model performance rather than only increasing volume.

Define labeling standards and taxonomies for robot episodes to ensure consistent capture of meaningful signal across runs and environments.

Translate real-world operational constraints and edge cases into data strategy decisions that drive concrete model improvements.

Create an organization-wide, real-time view of data lifecycle health to expose issues early and reduce downstream risk.

Convert deployment failures and rare scenarios into structured training and evaluation improvements for models and datasets.

Collaborate with ML researchers to design evaluation frameworks that reflect real operational conditions.

Work with annotation teams to refine annotation systems and quality checks that reduce noise in supervision.

Communicate data priorities clearly and set direction in fast-moving environments where urgency and ambiguity are common.

Coordinate with engineering to ensure that data pipelines, storage, and tooling support the data strategy efficiently.

Champion data-centric practices so that lifecycle analysis, metrics design, and cross-team influence become routine.

Use active learning and sampling strategies to make labeling efforts more targeted and cost-effective over time.

Maintain awareness of how data decisions propagate through the full stack, from collection through model deployment.

Identify and document best practices from past analyses so they can be reused across teams and products.

Support continuous improvement by measuring the impact of data strategy changes on downstream model outcomes.

Requirements

The posting states a bachelor's degree requirement. Bring 4-8+ years of experience in AI, robotics, autonomy, or data-centric systems roles to contextualize data strategy.

Demonstrate experience defining data quality standards, evaluation frameworks, annotation systems, or data strategy for machine learning products.

Collaborate effectively with cross-functional teams including ML researchers, operations, annotation teams, and engineering.

Convert deployment failures and edge cases into model training and evaluation improvements.

Communicate clearly and set priorities in fast-moving environments where urgency is common.

Show a strong ability to turn ambiguous problems into structured analyses that inform product and model decisions.

Have the persistence to drive data initiatives through complex stakeholder landscapes and competing priorities.

Nice to have

Bonus experience in fast-moving, ambiguous startup or R&D-heavy environments.

Background with embodied AI, video, or time-series data.

Familiarity with evaluation pipelines, active learning, or data-centric AI.

Exposure to annotation tools such as Labelbox, Scale, CVAT, Encord, or Voxel51.

Skills & tools

General AI and data strategy skills apply to this role, including lifecycle analysis, metrics design, and cross-team influence.

Practical notes

This role is based in Redwood City and is full-time. 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

Data strategy strongly influences model performance in embodied systems. Robotics workflows combine real-world operation data with simulation and labeling pipelines. Cross-functional collaboration drives priorities in fast-moving product environments. General familiarity with evaluation and annotation tooling supports effective data lifecycle management.

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.