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Data Annotation Manager Jobs in Bee Cave, TX (NOW HIRING)

AI Finance Expert - Remote

Austin, TX · Remote

$100 - $200/hr

Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ... management, or strategic finance. * Experience preparing investment memos, valuation analyses ...

Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...

Data Annotation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in strategy consulting, management consulting ...

... managers, analysts, and data annotation teams to define, build, evaluate, and continuously improve NLP models and language-based automation systems. Product Group Focus Areas The NLP product group is ...

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

... drift, and annotation error; active-learning sampling to target gaps; feedback loops from ... management, and lineage and metadata cataloging. Qualifications : Required : • 3+ years of ...

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

... drift, and annotation error; active-learning sampling to target gaps; feedback loops from ... management, and lineage and metadata cataloging. Qualifications : Required : • 3+ years of ...

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Data Annotation Manager information

See Bee Cave, TX salary details

$30.5K

$95.7K

$169.4K

How much do data annotation manager jobs pay per year?

As of Aug 31, 2026, the average yearly pay for data annotation manager in Bee Cave, TX is $95,661.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,000.00 and $123,600.00 per year, depending on experience, location, and employer.

What does a data annotation manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

What are the key skills and qualifications needed to thrive as a data annotation manager?

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

What are some common challenges faced by data annotation managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

What is the difference between Data Annotation Manager vs Data Labeling Specialist?

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What cities near Bee Cave, TX are hiring for Data Annotation Manager jobs?

Cities near Bee Cave, TX with the most Data Annotation Manager job openings:

Infographic showing various Data Annotation Manager job openings in Bee Cave, TX as of August 2026, with employment types broken down into 83% Full Time, 10% Part Time, and 7% Contract. Highlights an 76% In-person, 6% Hybrid, and 18% Remote job distribution, with an average salary of $95,661 per year, or $46 per hour.

AI Finance Expert - Remote

Austin, TX • Remote

$100 - $200/hr

Part-time

Posted 19 days ago


Job description

Job Title: AI Finance Domain Expert

Job Type: Contractor (Part-Time)
Location: Remote

Job Overview

We are seeking experienced AI Finance Domain Experts to contribute their financial expertise to an innovative project at the intersection of finance and artificial intelligence. In this role, you will help improve next-generation AI systems by reviewing, evaluating, and refining AI-generated financial content. No prior AI experience is required—your financial expertise, analytical skills, and professional judgment are what matter most.

Key Responsibilities
  • Analyze, review, and edit AI-generated financial content for accuracy, clarity, and relevance.

  • Develop, refine, and evaluate prompts related to financial analysis, valuation, and investment decision-making.

  • Assess and annotate financial data, reports, and AI-generated outputs using structured evaluation criteria.

  • Author and review investment memos, due diligence reports, research summaries, and technical documentation.

  • Evaluate AI outputs for logical consistency, factual accuracy, and adherence to professional financial standards.

  • Conduct independent research and fact-checking to validate financial information.

  • Provide detailed feedback to improve AI model performance and financial reasoning.

Required Skills
  • Critical Thinking

  • Analytical Reasoning

  • Quality Assurance

  • Prompt Engineering

  • AI Output Evaluation

  • Financial Analysis

  • Technical & Report Writing

  • Business Communication

  • Content Review & Editing

  • Fact Checking

  • Data Interpretation

  • Data Annotation

  • Problem-Solving

  • Independent Research

  • Attention to Detail

Preferred Qualifications
  • Minimum 3 years of experience in private equity, venture capital, investment banking, equity research, corporate development, investment management, or strategic finance.

  • Experience preparing investment memos, valuation analyses, financial models, due diligence reports, or market research.

  • Strong analytical, critical thinking, and problem-solving skills.

  • Excellent written communication and professional editing abilities.

  • Experience with prompt authoring, AI output evaluation, data annotation, or content review is a plus.

  • Master's, MBA, JD, PhD, or another advanced degree is preferred.

  • Commitment to producing high-quality, accurate, and well-documented work.