4 days ago

Data Analyst I

Talent Bridge

Hybrid
Full Time
$161,200
Hybrid

Job Overview

Job TitleData Analyst I
Job TypeFull Time
CategoryCommerce
Experience5 Years
DegreeMaster
Offered Salary$161,200
LocationHybrid

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Job Description

About the Role

We are seeking a detail-oriented Data Analyst I to support large-scale data curation and evaluation initiatives for advanced generative AI systems. This role focuses on improving model quality across key dimensions such as visual fidelity, prompt adherence, identity preservation, naturalness, and text generation within images.

You will work closely with engineers and research teams to manage data labeling workflows, maintain high-volume data pipelines, audit annotations, and analyze model outputs to identify quality gaps.

This is an onsite role requiring hands-on collaboration with technical teams in Menlo Park, CA.

Key Responsibilities

  • Data Curation & Labeling Operations: Manage end-to-end data labeling workflows, enqueue datasets for labeling, maintain labeling interfaces, extract structured labels for modeling teams, manually annotate training data, and audit/correct human-labeled data.
  • Data Engineering & Pipelines: Maintain and optimize large-scale data processing pipelines (billions of images), support data sourcing and content understanding using ML models, leverage LLMs to clean, annotate, and evaluate data, and assist in building efficient ETL workflows.
  • Data Governance: Maintain dataset portfolio with proper access controls, ensure compliance with data retention and privacy standards, and support governance and documentation practices.
  • Analysis & Model Evaluation: Identify model quality gaps using structured evaluation protocols, collaborate with engineers to summarize findings and recommend improvements, mine and prepare new datasets for iterative model training, and scale validated evaluation frameworks across product teams.

Required Qualifications

  • Associate’s degree or equivalent training in Computer Science, Engineering, Physics, Bioinformatics, or other STEM field.
  • Basic knowledge of Python and SQL.
  • Foundational understanding of computer vision and generative AI models.
  • Experience with data ETL workflows or pipelines.
  • Familiarity using LLMs for data labeling or evaluation tasks.
  • Strong attention to detail and analytical thinking.

Preferred Qualifications

  • Prior industry experience in software development, QA, or research.
  • Exposure to human-computer interaction or ML evaluation work.
  • Experience working in large-scale technology environments.
  • Strong written and verbal communication skills.

Work Environment

  • Onsite collaboration with engineering teams in Menlo Park, CA.
  • Fast-paced, research-driven environment.
  • High-impact role supporting next-generation AI systems.

Key skills/competency

  • Data Curation
  • Generative AI
  • Data Labeling
  • ETL Workflows
  • Python
  • SQL
  • Computer Vision
  • LLMs
  • Data Analysis
  • Model Evaluation

Tags:

Data Analyst
Data Curation
Data Labeling
Model Evaluation
Data Pipelines
AI Systems
Generative AI
Data Governance
ETL
Analytics
Annotation
Python
SQL
LLMs
Computer Vision
Machine Learning
Data Processing
AI Models
ETL Workflows
Generative Models
Data Tools

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How to Get Hired at Talent Bridge

  • Research Talent Bridge and its partners: Understand Talent Bridge's client base, especially those in AI/ML, and research their mission, values, and technological focus.
  • Tailor your resume for AI data roles: Customize your resume to highlight experience in data curation, ETL, Python, SQL, and generative AI, aligning with the Data Analyst I requirements.
  • Showcase your technical aptitude: Prepare to discuss your experience with data pipelines, LLMs, computer vision, and analytical problem-solving during technical interviews.
  • Emphasize collaboration and impact: Be ready to share examples of working with engineering teams, improving data quality, and contributing to high-impact AI projects.
  • Highlight attention to detail: Demonstrate your meticulous approach to data auditing, annotation, and identifying quality gaps crucial for this Data Analyst I position.

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