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 ...
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 ...
$18 - $23.50/hr
You will be working at the level of expert linguistic judgment. This is not data entry. Decisions ... Comfortable with web-based annotation platforms and variable-speed audio playback * Reliable high ...
$18 - $23.50/hr
You will be working at the level of expert linguistic judgment. This is not data entry. Decisions ... Comfortable with web-based annotation platforms and variable-speed audio playback * Reliable high ...
Linguistic Data Annotation information
What are some common challenges faced by linguistic data annotators, and how can they be addressed?
Linguistic data annotation often involves interpreting ambiguous or context-dependent language, which can be challenging, especially when dealing with idiomatic expressions, slang, or multiple languages. Consistency in labeling is critical, so annotators must regularly review guidelines and communicate with team members to resolve uncertainties. Many teams use collaborative tools and periodic calibration sessions to ensure high-quality, uniform annotations. Staying detail-oriented and open to feedback helps annotators continuously improve their work and adapt to evolving project requirements.
What is linguistic data annotation?
Linguistic data annotation is the process of labeling or tagging language data, such as text or speech, with relevant linguistic information. This can include marking parts of speech, named entities, syntactic structures, or semantic roles to help train and evaluate natural language processing (NLP) models. Annotators follow specific guidelines to ensure consistency and accuracy, making the data usable for machine learning tasks. Linguistic data annotation is essential for developing AI applications like chatbots, translation systems, and speech recognition software.
What is the difference between Linguistic Data Annotation vs Data Labeling Specialist?
| Aspect | Linguistic Data Annotation | Data Labeling Specialist |
|---|---|---|
| Credentials | Basic understanding of linguistics, language skills | General data labeling skills, attention to detail |
| Work Environment | Tech companies, AI development teams | Data annotation firms, AI/ML companies |
| Industry Usage | Natural language processing, speech recognition | Computer vision, image and video annotation |
| Search/Comparison Intent | Understanding linguistic annotation roles | General data labeling roles |
While both roles involve preparing data for AI models, Linguistic Data Annotation focuses on language-specific tasks like transcribing, tagging, and annotating text or speech data. Data Labeling Specialists handle a broader range of data types, including images and videos, with less emphasis on linguistic expertise. The roles often overlap in AI development but differ in the type of data and skills required.
What are the key skills and qualifications needed to thrive as a linguistic data annotator?
To thrive as a Linguistic Data Annotator, you need a strong grasp of linguistics, attention to detail, and proficiency in at least one language, often supported by a degree in linguistics or a related field. Familiarity with annotation tools, text analysis software, and basic data management systems is typically required. Exceptional analytical thinking, consistency, and the ability to follow detailed guidelines are crucial soft skills in this position. These skills ensure high-quality, accurate data that directly supports the development and training of language technologies.
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Job description
hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.
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.
About J.P. Morgan
Sourced by ZipRecruiter
Industry
Banking and credit intermediation
Company size
10,000+ Employees
Headquarters location
New York, NY, US