Data Analyst I--Onsite – Menlo Park, CA (MPK)

itD Tech•Menlo Park, CA

7d•Onsite

About The Position

itD is seeking a Data Analyst I to support large-scale data curation, evaluation, and analytics efforts that directly improve the quality of next-generation generative AI models. This role contributes to the success of advanced AI systems by building and maintaining high-volume data pipelines, managing annotations, and identifying data gaps that impact model performance across visual quality, prompt adherence, identity preservation, and naturalness. The ideal candidate brings foundational experience in data analytics, data engineering workflows, and machine learning–adjacent systems, along with a strong attention to detail and the ability to work hands-on with complex datasets at scale.

Requirements

1–2 years of professional experience in data analytics, data engineering, or a related technical role.
Working knowledge of Python and SQL.
Basic understanding of computer vision concepts and generative models.
Experience or familiarity with data ETL workflows and data pipelines.
Experience using LLMs or machine learning models for data labeling, evaluation, or analysis.
Strong verbal and written communication skills with the ability to collaborate cross-functionally.
Ability to work onsite in Menlo Park, CA, and manage tasks independently.

Nice To Haves

Prior experience in software development, software testing, or human–computer interaction research.
Previous experience supporting large-scale machine learning or AI-related data initiatives.
Prior experience working in fast-paced, research-driven environments.

Responsibilities

Manage end-to-end data curation and labeling workflows, including data queueing, annotation processes, and extraction of labeled datasets for modeling teams.
Maintain and support large-scale data processing pipelines handling billions of images, including data sourcing, transformation, and enrichment.
Apply machine learning models and large language models (LLMs) to analyze, clean, and label training data at scale.
Govern datasets by ensuring data access controls, retention policies, and privacy compliance are consistently met.
Perform manual and automated data annotations based on model requirements and conduct audits to evaluate annotation quality.
Collaborate with engineers and researchers to analyze evaluation results, identify model quality gaps, and determine future data needs.
Prepare curated datasets to support iterative model training and evaluation cycles.
Attend regular internal practice community meetings.
Collaborate with your itD practice team on industry thought leadership.
Complete client case studies and learning material (blogs, media material).
Build out material to contribute to the Digital Transformation practice.
Attend internal itD networking events (in person and virtual).
Work with leadership on career fast-track opportunities.