Data Studio Lead - Mechanical Engineering

Key Responsibilities

• Data Operations & Delivery:
• Own the definition and evaluation of output quality, ensuring consistency and accountability across all internal and external datasets.
• Oversee ingestion, cleaning, transformation, and structuring of customer data for AI model training and inference.
• Build scalable processes and frameworks for data labeling, annotation tools, quality checks, and feedback loops.
• Ensure timely, high quality data delivery for internal R&D and customer deployments.
• Own SLAs, accuracy targets, and “definition of quality” for all datasets.


• Product & Solution Leadership: 
• Partner with Product Managers to shape data-related features, workflows, and tooling (e.g., annotation UI, correction UI, model output review tools).
• Translate customer needs into clear data requirements and repeatable solutions.


• Team & Process Leadership:
• Build and lead a Data Studio team.
• Implement best practices for documentation, quality metrics, repeatable workflows, and compliance.
• Drive continuous improvements in throughput, cost efficiency, and accuracy.


• Cross-Functional Collaboration: 
• Work closely with engineering and research teams to define data needs for model training, evaluation, and debugging.
• Collaborate with BD team to scope data requirements for onboarding and implementation.
• Serve as the subject matter expert on data quality, data formats, labeling standards, and customer specific nuances related to manufacturing design.


• Customer & Project Management: 
• Interface with customers to understand their domain, data structures, and quality expectations.
• Guide customers through data preparation, schema definition, and ongoing iteration.
• Own delivery timelines, reporting, and communication for all active data projects.

Required Qualifications

• Domain expertise in manufacturing design or industrial workflows; mechanical engineering background is required.
• Hands-on experience with CAD tools (e.g., SolidWorks, CATIA, Siemens NX, Creo) and familiarity with interpreting complex mechanical models.
• Experience creating or working with technical documentation such as exploded views, work-step sequencing, GD&T drawings, repair manuals, and similar manufacturing/assembly artifacts.
• Ability to work directly with customers and translate domain problems into data requirements.
• Experience leading and growing teams.
• Excellent project management and cross-functional communication skills.

Preferred Qualifications

• Familiarity with annotation tools and Illustration tools (e.g., Creo Illustrate, Adobe Illustrator, Arbortext)
• Experience in data operations, data analytics, ML operations, or related fields.
• Strong understanding of AI/ML data lifecycle: collection -> labeling -> QA -> training -> evaluation -> deployment.
• Ability to partner with product managers to shape data tooling and internal UIs.
• Experience in a startup or high-growth tech environment.

What Success Looks Like

• High-quality, on-time data delivery for every customer and internal teams.
• Increasing automation and decreasing manual effort in the data pipeline.
• Clear standards for data quality that align with model performance needs.
• Data Studio becomes a strategic partner for customers in automating their workflows.