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No Experience Machine Learning Data Annotation Jobs in Delaware

Instacart is a Flex First team There's no one-size fits all approach to how we do our best work ... self study and experience * Have strong programming skills in Python and fluency in data ...

Instacart is a Flex First team There's no one-size fits all approach to how we do our best work ... self study and experience * Have strong programming skills in Python and fluency in data ...

Instacart is a Flex First team There's no one-size fits all approach to how we do our best work ... self study and experience * Have strong programming skills in Python and fluency in data ...

Instacart is a Flex First team There's no one-size fits all approach to how we do our best work ... self study and experience * Have strong programming skills in Python and fluency in data ...

Instacart is a Flex First team There's no one-size fits all approach to how we do our best work ... self study and experience * Have strong programming skills in Python and fluency in data ...

Instacart is a Flex First team There's no one-size fits all approach to how we do our best work ... self study and experience * Have strong programming skills in Python and fluency in data ...

Instacart is a Flex First team There's no one-size fits all approach to how we do our best work ... self study and experience * Have strong programming skills in Python and fluency in data ...

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No Experience Machine Learning Data Annotation information

What should I expect when collaborating with machine learning engineers as a data annotator with no prior experience?

As a data annotator working alongside machine learning engineers, you will play a vital role in preparing high-quality labeled data for model training. Engineers often provide clear guidelines and feedback on how to label or categorize data accurately, and they may hold regular check-ins to address questions and ensure consistency. While you may not need technical expertise, strong communication and attention to detail are essential, as your work directly impacts the performance of machine learning models. Over time, you’ll become familiar with annotation tools and may have the opportunity to take on more advanced tasks or quality assurance responsibilities.

What are 'No Experience Machine Learning Data Annotation' jobs?

'No Experience Machine Learning Data Annotation' jobs are entry-level positions where individuals help label and categorize data used to train machine learning models. These roles do not require prior experience in data science or programming, making them accessible to beginners. Typical tasks may include tagging images, transcribing audio, or identifying objects in videos. These jobs are essential for improving the accuracy of AI systems and are often done remotely or on a flexible schedule.

What are the key skills and qualifications needed to thrive as a No Experience Machine Learning Data Annotation Specialist, and why are they important?

To succeed in a No Experience Machine Learning Data Annotation role, you need strong attention to detail, basic computer literacy, and the ability to follow precise instructions, often requiring at least a high school diploma. Familiarity with data labeling tools (like Labelbox or Supervisely) and experience with spreadsheet software are typically helpful, though many positions offer on-the-job training. Reliability, patience, and effective communication are valuable soft skills for maintaining quality and meeting deadlines. These skills ensure accurate, consistent data labeling, which is critical for training reliable machine learning models.

What is the difference between No Experience Machine Learning Data Annotation vs Data Labeling Specialist?

AspectNo Experience Machine Learning Data AnnotationData Labeling Specialist
Required CredentialsNo formal experience needed, training providedTypically similar, may require basic technical skills
Work EnvironmentRemote or office-based, repetitive tasksRemote or onsite, focused on data preparation
Industry UsageCommon in AI/ML companies, tech startupsUsed across tech, automotive, healthcare sectors
Search & Comparison IntentOften searched by beginners or entry-level job seekersCompared for skill requirements and job scope

Both roles involve labeling data for machine learning models, with minimal experience required. Data Labeling Specialists may have slightly more specialized tasks, but both are entry-level positions vital for AI development.

What are popular job titles related to No Experience Machine Learning Data Annotation jobs in Delaware? For No Experience Machine Learning Data Annotation jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching No Experience Machine Learning Data Annotation jobs in Delaware look for? The top searched job categories for No Experience Machine Learning Data Annotation jobs in Delaware are:
What cities in Delaware are hiring for No Experience Machine Learning Data Annotation jobs? Cities in Delaware with the most No Experience Machine Learning Data Annotation job openings:
Infographic showing various No Experience Machine Learning Data Annotation job openings in Delaware as of July 2026, with employment types broken down into 2% Locum Tenens, 33% Full Time, 27% Part Time, 1% Contract, 36% Nights, and 1% Summer. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution.
Data Domain Architect Lead

Data Domain Architect Lead

JPMorgan Chase & Co.

Wilmington, DE • On-site

Full-time

Medical, Retirement

Posted 21 days ago


JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 492 frontline employees who took The Breakroom Quiz

61st of 150 rated banks


Job description


Machine Learning and Artificial Intelligence play a critical role in transforming Consumer and Community Banking Operations. The ability to utilize data in meaningful ways allows us to develop solutions which both our customers and employees can benefit from. Customers expect tailored servicing and Chase is looking to deliver personalization to meet their needs. This is powered by high-quality annotated data and detailed annotation schemes that are the backbone of impactful Artificial Intelligence/Machine Learning ( AI/ML)L algorithms and applications.
As a Data Domain Architect Lead within the Data Annotation team , you will use your domain expertise and people-leading experience to partner your team closely with teams in Data Science, Analytics, and Engineering to develop machine learning solutions. This will involve the collection, curation, annotation, enrichment, and validation of data and the development of taxonomies and other linguistic resources to help train machine learning models, drive insight, analysis, and possible content creation.
Job 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

Required qualifications, capabilities, and skills
  • 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 qualifications, capabilities, and skills
  • 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

About Us
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
About the Team
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.
The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

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