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Freelance Machine Learning Data Annotation Jobs in Alabama

Varsity Tutors

Data Science Tutor

Tuscaloosa, AL · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Varsity Tutors

Data Science Tutor

Huntsville, AL · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Varsity Tutors

Data Science Tutor

Montgomery, AL · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Varsity Tutors

Data Science Tutor

Birmingham, AL · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

4pconsultinginc

Data Scientist III (IT)

Birmingham, AL · On-site

This role requires strong expertise in statistics, machine learning, and programming , with the ability to transform raw data into actionable insights. The ideal candidate has hands-on experience ...

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Freelance Machine Learning Data Annotation information

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

To thrive as a Freelance Machine Learning Data Annotation specialist, you need attention to detail, basic knowledge of data labeling concepts, and familiarity with machine learning data types. Experience with annotation tools (such as Labelbox, RectLabel, or CVAT) and understanding of data privacy protocols are commonly required. Strong communication, time management, and the ability to follow complex guidelines are essential soft skills for delivering accurate results. These skills ensure high-quality, consistent data annotation, which is critical for effective machine learning model training and performance.

What are some common challenges faced by freelance machine learning data annotators, and how can they be managed?

Freelance machine learning data annotators often encounter challenges such as maintaining data accuracy, handling repetitive tasks, and understanding complex annotation guidelines. Staying organized and regularly reviewing project instructions can help ensure consistency and quality in annotations. Additionally, communicating proactively with project managers and utilizing annotation tools efficiently can help manage workload and clarify uncertainties. Building expertise in different data types (text, image, audio) also allows annotators to diversify their projects and reduce monotony.

What is freelance machine learning data annotation?

Freelance machine learning data annotation involves labeling or tagging data—such as images, text, audio, or video—to help train machine learning models. As a freelancer, you work independently or through platforms, completing specific annotation tasks assigned by companies or researchers. This work is essential because high-quality labeled data is required for AI systems to learn and make accurate predictions. Annotators may categorize images, transcribe speech, or highlight relevant information in documents. The flexibility of freelancing allows you to choose projects and work remotely.

What is the difference between Freelance Machine Learning Data Annotation vs Data Labeler?

AspectFreelance Machine Learning Data AnnotationData Labeler
CredentialsBasic understanding of annotation tools, sometimes with specialized domain knowledgeTypically no formal credentials required
Work EnvironmentRemote, flexible, project-basedOften remote or in-house, depending on employer
Industry UsageUsed in AI/ML development for training datasetsUsed in data preparation for various industries, including AI
Search/Comparison IntentFocuses on freelance opportunities, project scope, and toolsMore general, often employed by companies for data labeling tasks

Freelance Machine Learning Data Annotation involves independently completing annotation tasks for AI models, often with specialized tools and domain knowledge. Data Labelers typically perform similar tasks but may work as employees or contractors within a company. The main difference lies in the freelance nature and project-based work of data annotation roles.

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What cities in Alabama are hiring for Freelance Machine Learning Data Annotation jobs? Cities in Alabama with the most Freelance Machine Learning Data Annotation job openings:
Freelance Machine Learning Data Annotation jobs near you
Data Scientist (MSIC) with Security Clearance

Data Scientist (MSIC) with Security Clearance

COLSA CORP.

Huntsville, AL • On-site

Other

Posted 12 days ago

Job description

COLSA Corporation is currently seeking a Data Scientist to join our MSIC Team. We are seeking a self-motivated individual that is eager to learn and help the team support the critical junction between managing high-volume data and mission-essential operations. This opportunity will allow you to run basic analyses and visualizations to surface actionable insights, and coordinate with program teams to translate analytical outputs into operational plans and process improvements; ideal candidates are early to mid career data scientists with hands-on experience in Python or R, SQL, data wrangling, and version control, strong attention to detail, excellent communication skills, and a proactive mindset for balancing fast-paced operational needs with rigorous data practices.

Principal Duties and Responsibilities (Essential functions) Performs analysis of data systems, such as Big Data systems * * May perform statistical analysis and provide input for reports and dashboards Involved with the development of data products, reports, and dashboards or other display techniques Evaluates data, algorithms, and their interaction to improve algorithm performance May apply advanced statistics, including natural language processing and machine learning to create solutions. May assist in data modeling and data virtualization May write code to preprocess and clean data At COLSA, people are our most valuable resource and centered at our core value. We invite you to unite your talents with opportunity and be a part of our "Family of Professionals!" Learn about our employee-centric culture and benefits https://www.colsa.com/culture_benefits/ Required Experience Requirements Bachelors degree or higher in computer science, data science, engineering, math, statistics, operations research or related field or equivalent experience Minimum of 7 to 10 years related experience Familiarity with advanced machine learning, data science techniques and mathematical approaches Working knowledge of current operating systems and programming languages U.S, Citizenship required: Active DoD Top Secret security clearance with eligibility for DIA-SCI access.

Candidate selected must successfully pass a DIA CI polygraph within 60 days of hire Preferred Qualifications Master's degree in Computer Science, Data Science, Engineering, Math, Statistics, Operations Research or related field or equivalent experience 2+ years programming experience in Python, R, Julia, TensorFlow, CUDA, JavaScript, Scala, Java, Unix/Linux, C, C++ Proactive self-starter capable of finding and solving problems with little guidance Experience implementing and utilizing Generative AI (such as large language models (LLMs)) Experience designing, building, and maintaining scalable, reliable data pipelines (ETL/ELT workflows) Familiarity with MLOps best practices * Knowledge of containerization (Docker, Podman) Applicant selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information. COLSA Corporation is an Equal Opportunity Employer, Minorities/Females/Veterans/Disabled. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.

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