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Data Annotation Manager Jobs in Philadelphia, PA

URBN

URBN Senior Data Scientist

Philadelphia, PA · On-site

The role involves close collaboration with leadership, Product Management, Creative, and ... annotation workflows, and synthetic data generation. * Analyze and benchmark model outputs for ...

URBN

URBN Senior Data Scientist

Philadelphia, PA

The role involves close collaboration with leadership, Product Management, Creative, and ... annotation workflows, and synthetic data generation. * Analyze and benchmark model outputs for ...

Slipstream IT

Statistical Analytical Programmer

PA · On-site +1

Strong documentation skills, including code annotation, traceability, and reproducibility ... manage timelines effectively. Preferred Qualifications: * Experience with R for data analysis ...

Synchrony Group

Group Copy Supervisor

West Chester, PA · On-site +1

Partner with internal teams to turn complex data into engaging and meaningful copy for a variety of ... Ensure high quality and degree of accuracy with thorough referencing and annotation of all ...

Gap International

Linguist

Philadelphia, PA

ABOUT THE ROLE Global management consulting firm Gap International , based in the Philadelphia area ... Strong analytic and synthesis skills; able to move from theory → data → insight → application

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All JobsData Annotation JobsData Annotation Manager JobsData Annotation Manager Jobs in Philadelphia, PA

Data Annotation Manager information

See Philadelphia, PA salary details

$31.3K

$98K

$173.6K

How much do data annotation manager jobs pay per year?

As of Jul 27, 2026, the average yearly pay for data annotation manager in Philadelphia, PA is $98,027.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,600.00 and $126,600.00 per year, depending on experience, location, and employer.

What is the salary of data annotation manager?

The salary of a data annotation manager typically ranges from $60,000 to $120,000 annually, depending on experience, location, and company size. Senior roles or those in high-cost areas may offer higher compensation, and familiarity with annotation tools and team management can influence pay levels.

How much do data annotation project managers make?

Data annotation project managers typically earn between $60,000 and $100,000 annually, depending on experience, location, and company size. They oversee annotation teams, coordinate workflows, and ensure quality standards are met, often requiring familiarity with annotation tools and project management skills.

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 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.

Does data annotation actually pay well?

Data annotation managers typically earn competitive salaries that reflect their experience and responsibilities, often ranging from entry-level to senior roles. Compensation can vary based on industry, location, and company size, with specialized skills in tools like labeling platforms and quality control often leading to higher pay.

What are the key skills and qualifications needed to thrive as a Data Annotation Manager, and why are they important?

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.

How hard is it to get hired by data annotation?

Getting hired as a data annotation manager typically requires relevant experience in data labeling, familiarity with annotation tools, and strong organizational skills. The hiring process often involves reviewing previous work, technical assessments, and demonstrating attention to detail, with opportunities available in companies that outsource data labeling tasks.

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 are the most commonly searched types of Data Annotation jobs in Philadelphia, PA? The most popular types of Data Annotation jobs in Philadelphia, PA are:
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What job categories do people searching Data Annotation Manager jobs in Philadelphia, PA look for? The top searched job categories for Data Annotation Manager jobs in Philadelphia, PA are:
What cities near Philadelphia, PA are hiring for Data Annotation Manager jobs? Cities near Philadelphia, PA with the most Data Annotation Manager job openings:
Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in Philadelphia, PA as of July 2026, with employment types broken down into 2% Locum Tenens, 34% Full Time, 26% Part Time, 2% Contract, 35% Nights, and 1% Summer. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution, with an average salary of $98,027 per year, or $47.1 per hour.

Data Domain Architect Lead

JPMorganChase

Wilmington, DE • On-site

Full-time

Posted 23 days ago

Job description

Job Summary:
JPMorganChase is a leading financial services firm, helping nearly half of America’s households and small businesses achieve their financial goals. As a Data Domain Architect Lead, you will manage a team to develop machine learning solutions through data annotation, curation, and validation while collaborating with other teams to optimize training data for machine learning models.
Responsibilities:
• Manage and coach a team of Machine Learning Data Domain analysts to support data annotation and label data/content using annotation tools and analysis
• Partner with leads in Data Science, Engineering, and Analytics to develop strategies to optimize training data for machine learning models
• Lead efforts to identify patterns and trends in conversational data through Natural Language Processing and/or other computational linguistic approaches
• Collaborate with stakeholders on evaluating the quality of machine learning classification and other output
• Actively contribute to the team’s continuous learning mindset by bringing in new ideas and perspectives that stretch the thinking of the group
Qualifications:
Required:
• 6+ years of related experience in development of machine learning solutions
• Familiar with industry annotation and labeling methods
• Experience with various data modeling techniques and tools
• Familiar with Finance and Banking products
• Broad expertise in data technologies; i.e., data warehousing, data processing, data quality concepts, Business Intelligence tools and analytical tools, unstructured data, machine learning
• Excellent analytical and problem-solving skills and the ability to pay close attention to detail
• Experience using Python in working with and analyzing large real-world datasets
• Working knowledge of information and data retrieval
• Working knowledge of machine learning and artificial intelligence paradigms and libraries
• Familiar with Large Language Models (LLMs) and prompt engineering
Preferred:
• Masters or PhD in a related field, or Bachelors
• Technical understanding of common relational database systems; i.e., Teradata and Oracle
• Excellent command of the Structured Query Language (SQL)
• Knowledge of SAS or Scala, and Python languages
• Knowledge of Advanced Statistics
• Advanced analytical thinking and problem-solving skills
• Strong interpersonal & communication skills
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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