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Flexible Data Annotation Analyst Jobs in Delaware

JPMorganChase

Data Domain Architect Lead

Wilmington, DE · On-site

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 ...

Fairygodboss

Data Domain Architect Lead

Wilmington, DE · On-site

$120 - $150/hr

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 ...

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Flexible Data Annotation Analyst information

What is a flexible data annotation analyst?

A Flexible Data Annotation Analyst is a professional responsible for labeling, categorizing, and tagging data—such as text, images, audio, or video—to prepare it for use in machine learning and artificial intelligence projects. The 'flexible' aspect typically means the role allows for remote work, adjustable hours, or project-based assignments. Analysts use specific tools and follow detailed guidelines to ensure data quality and consistency. This role is crucial for training accurate AI models, as well-annotated data helps improve the performance of automated systems.

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

To thrive as a Flexible Data Annotation Analyst, you need keen attention to detail, analytical thinking, and a basic understanding of data labeling processes, often supported by a high school diploma or relevant experience. Familiarity with annotation tools such as Labelbox, Prodigy, or similar platforms, as well as basic proficiency in spreadsheet software, is typically required. Strong time management, adaptability, and clear communication skills help you deliver accurate results and work effectively with remote teams. These abilities ensure high-quality, consistent data labeling that is critical for training reliable machine learning models.

How does a flexible data annotation analyst typically collaborate with other teams to ensure data quality?

As a Flexible Data Annotation Analyst, you will frequently interact with data scientists, machine learning engineers, and project managers to clarify annotation guidelines and resolve ambiguities in the data. Collaboration often involves participating in virtual meetings, providing feedback on annotation tools, and reporting inconsistencies or uncertainties encountered during the labeling process. This teamwork ensures that annotated datasets meet project standards and contribute to high-quality machine learning outcomes. Regular communication and openness to feedback are key to success in this collaborative environment.

Can I work as a flexible data annotation analyst with no experience?

Flexible data annotation analyst roles often do not require prior experience, as training is typically provided to teach the necessary skills and tools. Basic computer literacy and attention to detail are usually sufficient to start, making it accessible for beginners interested in data labeling tasks.

Do data annotation jobs offer flexible hours?

Data annotation jobs often offer flexible hours, allowing workers to choose when they complete tasks, especially in freelance or remote roles. However, some positions may have deadlines or specific schedules depending on the employer or project requirements.

Does data annotation actually pay well?

Data annotation analysts typically earn hourly wages that are close to minimum wage or slightly above, depending on the platform and complexity of tasks. Pay rates can vary based on experience, skill level, and the employer, but generally, it is not considered a high-paying role. Many positions are freelance or part-time, which can impact overall earnings.

Is it hard to get hired for a flexible data annotation analyst?

Getting hired as a flexible data annotation analyst generally depends on having basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require extensive experience, making the role accessible to a wide range of candidates. However, competition can vary based on the employer and location, and some roles may prefer candidates with prior experience or specific technical knowledge.

What are the most commonly searched types of Data Annotation Analyst jobs in Delaware?

The most popular types of Data Annotation Analyst jobs in Delaware are:

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For Flexible Data Annotation Analyst jobs in Delaware, the most frequently searched job titles are:

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Flexible Data Annotation Analyst jobs near you

Data Domain Architect Lead

JPMorganChase

Wilmington, DE • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

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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