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Full Time Machine Learning Data Annotation Jobs in Indiana

STI

Data Analyst

Indianapolis, IN · On-site

Data Analyst is responsible for collecting, processing, and analyzing data to help make data-driven ... Experience in machine learning, predictive modeling, or statistical analysis. Knowledge of database ...

Toptal

Qualifications and Requirements * 5+ years of professional experience in Data Science, Machine Learning, or Applied ML roles. * Demonstrated experience operating as the sole or lead Data Scientist on ...

Tao Digital Solutions, Inc.

Senior ML Engineer

Indianapolis, IN · On-site

$99.90K - $137.20K/yr

We are building a small, high-impact team to support advanced analytics and machine learning ... Design and implement data pipelines for large-scale healthcare datasets (EHR, claims, RTLS, device ...

SS&C

Product Owner- Operations

Evansville, IN · Hybrid

Partner with technical architecture, development, and data science teams to optimize machine learning models for reconciliation and operational workflows. * Define and maintain the product backlog ...

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Full Time Machine Learning Data Annotation information

What are the key skills and qualifications needed to thrive as a Full Time Machine Learning Data Annotation Specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What are Full Time Machine Learning Data Annotation jobs?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Indiana? For Full Time Machine Learning Data Annotation jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Full Time Machine Learning Data Annotation jobs in Indiana look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs in Indiana are:
What cities in Indiana are hiring for Full Time Machine Learning Data Annotation jobs? Cities in Indiana with the most Full Time Machine Learning Data Annotation job openings:
Full Time Machine Learning Data Annotation jobs near you

Data Analyst

STI

Indianapolis, IN • On-site

Full-time

Posted 5 days ago

Job description

Data Analyst is responsible for collecting, processing, and analyzing data to help make data-driven decisions. This role involves working with large datasets, identifying trends, creating reports, and providing actionable insights to stakeholders.
Job Summary:
The Data Analyst is responsible for collecting, processing, and analyzing data to help organizations make data-driven decisions. This role involves working with large datasets, identifying trends, creating reports, and providing actionable insights to stakeholders.
Key Responsibilities:
Gather, clean, and organize large datasets from various sources.
Validate and verify data accuracy, consistency, and completeness across various data sources.
Identify and resolve data discrepancies, inconsistencies and errors.
Performa routine and ad hoc data quality checks using automated and manual validation techniques.
Work closely with data entry teams and other analysts and stakeholders to ensure data integrity.
Perform data analysis to identify trends, patterns, and correlations.
Develop reports, dashboards, and visualizations using tools like Excel, SQL, Tableau, or Power BI.
Collaborate with cross-functional teams to support business objectives.
Interpret data to provide strategic recommendations and business insights.
Ensure data accuracy and integrity.
Use statistical techniques and predictive modeling to improve decision-making.
Document processes and methodologies for data collection and analysis.
Required Skills & Qualifications:
Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, or a related field.
Proficiency in data analysis tools such as SQL, Python, R, or Excel.
Experience with data visualization tools like Tableau, Power BI, or Google Data Studio.
Strong analytical and problem-solving skills.
Excellent communication and presentation abilities.
Ability to work independently and in a team-oriented environment.
Attention to detail and a strong understanding of data governance principles.
Preferred Qualifications:
Experience in machine learning, predictive modeling, or statistical analysis.
Knowledge of database management and ETL (Extract, Transform, Load) processes.
Familiarity with cloud platforms such as AWS, Google Cloud, or Azure.

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