10 days ago

Data Analyst I

Keystone Recruitment

Remote
Full Time
$166,400
Remote

Job Overview

Job TitleData Analyst I
Job TypeFull Time
CategoryCommerce
Experience5 Years
DegreeMaster
Offered Salary$166,400
LocationRemote

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Job Description

Data Analyst I at Keystone Recruitment

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 a fast-paced, research-driven environment.

Key Responsibilities

  • Data Curation & Labeling Operations
    • Manage end-to-end data labeling workflows
    • Enqueue datasets for labeling and maintain labeling interfaces
    • Extract structured labels for modeling teams
    • Manually annotate training data when required
    • Audit and 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
    • Assist in building efficient ETL workflows
  • Data Governance
    • Maintain dataset portfolio with proper access controls
    • Ensure compliance with data retention and privacy standards
    • 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
    • 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
  • Data Labeling
  • Generative AI
  • LLMs
  • Python
  • SQL
  • ETL Workflows
  • Computer Vision
  • Model Evaluation
  • Data Governance

Tags:

Data Analyst
data curation
data labeling
model evaluation
data pipelines
data governance
analytics
AI support
annotation
ETL
quality assurance
Python
SQL
LLMs
generative AI
computer vision
data processing
machine learning
ETL tools
cloud platforms

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How to Get Hired at Keystone Recruitment

  • Research Keystone Recruitment's AI focus: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor, specifically regarding their advanced generative AI initiatives.
  • Tailor your resume for Data Analyst I: Customize your application to highlight experience in data curation, labeling, ETL, Python, and SQL, emphasizing relevance to AI model quality.
  • Showcase technical proficiency: Prepare to demonstrate your basic knowledge of Python and SQL, foundational understanding of computer vision and generative AI, and experience with data pipelines.
  • Prepare for detailed analytical questions: Be ready to discuss how you identify data quality gaps, your attention to detail in auditing annotations, and your collaborative approach with engineering teams.
  • Understand the onsite environment: Although initially stated remote, the role is explicitly onsite in Menlo Park, CA, so prepare to articulate how you thrive in a collaborative, fast-paced, research-driven environment.

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