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Entry Level Data Classification Jobs (NOW HIRING)

SynergisticIT

Entry Level Data Scientiest

Manchester, NH · On-site

$16.25 - $21.75/hr

Identify valuable data sources and automate collection processes * Undertake preprocessing of ... NLP models, Deep Learning models for image classification Desired Candidate Profile Skills required:

SynergisticIT

Entry Level Data Scientiest

Los Angeles, CA · On-site

$18 - $24/hr

Identify valuable data sources and automate collection processes * Undertake preprocessing of ... NLP models, Deep Learning models for image classification Desired Candidate Profile Skills required:

TEKsystems

Entry Level Data Analyst

West Harrison, NY

$20 - $21/hr

Duties in the job include transferring data using Microsoft excel and sorting it with intermediate ... Eligibility requirements apply to some benefits and may depend on your job classification and ...

New

TEKsystems

Data Center Technician

Fort Wayne, IN

$20 - $21/hr

Entry-Level Data Center Technician Ready to start a career in telecom and data centers? We're ... Eligibility requirements apply to some benefits and may depend on your job classification and ...

New

TEKsystems

Entry Data Center Technician

Fort Wayne, IN

$20 - $22/hr

Description Seeking an Entry level data center technicians to install cable and fiber at the data ... Eligibility requirements apply to some benefits and may depend on your job classification and ...

New

TEKsystems

Data Center Technician

Fort Wayne, IN

$20 - $21/hr

Entry-Level Data Center Technician Launch a New Career in Technology -- No Data Center Experience ... Eligibility requirements apply to some benefits and may depend on your job classification and ...

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Entry Level Data Classification information

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How much do entry level data classification jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for entry level data classification in the United States is $19.47, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $21.88 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Entry Level Data Classification Specialist, and why are they important?

To thrive as an Entry Level Data Classification Specialist, you need strong attention to detail, analytical skills, and a basic understanding of data privacy concepts, often supported by a relevant degree or coursework. Familiarity with data management software, spreadsheet tools like Microsoft Excel, and sometimes data labeling platforms is typically required. Being organized, dependable, and having clear communication skills help you excel in this role. These competencies ensure accurate data categorization, compliance with regulations, and efficiency in managing large volumes of information.

What are some common challenges faced in an entry-level data classification role and how can I prepare for them?

In an entry-level data classification role, you may encounter challenges such as handling large volumes of data, ensuring consistency in labeling, and adapting to evolving classification criteria. It's important to develop strong attention to detail and become familiar with the tools and guidelines used by your team. Seeking feedback from experienced colleagues and actively participating in team discussions can help you quickly adapt and maintain accuracy. Over time, you'll gain efficiency and confidence as you learn best practices and become more comfortable with the data sets.

What is the difference between Entry Level Data Classification vs Data Analyst?

AspectEntry Level Data ClassificationData Analyst
Required CredentialsHigh school diploma or equivalent; some roles may prefer certifications in data managementBachelor's degree in data science, statistics, or related field; certifications like Microsoft Excel or Tableau are common
Work EnvironmentData management teams, administrative offices, or IT departmentsBusiness environments, analytics teams, or consulting firms
Employer & Industry UsageUsed across industries for organizing data sets, often entry-level roles in data managementUsed in various industries for interpreting data, generating reports, and supporting decision-making

Entry Level Data Classification focuses on organizing and categorizing data, often requiring basic data management skills. Data Analysts analyze data to extract insights, requiring more advanced analytical skills and tools. While both roles work with data, Data Analysts typically have more technical expertise and responsibilities in data interpretation.

What is an Entry Level Data Classification job?

An Entry Level Data Classification job involves organizing and categorizing data according to specific guidelines or criteria. Employees in this role typically review data sets, label information appropriately, and ensure data is stored accurately for easy retrieval or analysis. This work helps companies manage large volumes of information, improve data quality, and support business decision-making. No advanced technical skills are usually required, but attention to detail and basic computer proficiency are important.
What are the most commonly searched types of Data Classification jobs? The most popular types of Data Classification jobs are:
What job categories do people searching Entry Level Data Classification jobs look for? The top searched job categories for Entry Level Data Classification jobs are:
Entry Level Data Classification jobs near you
Infographic showing various Entry Level Data Classification job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 68% Full Time, 27% Part Time, 1% Temporary, and 3% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $40,504 per year, or $19.5 per hour.
Jr. Data Scientist 00021

Jr. Data Scientist 00021

West Coast Consulting LLC

Westbrook, ME • On-site

Other

Medical, Life

Posted 7 days ago

New

Job description

Job Description
Hybrid -Westbrook, ME
Job Description:
The Machine Intelligence team in R&D is looking for an entry-level Data Scientist to develop machine learning solutions for the hematology analyzers. In this role, you will work on classification and clustering problems on tabular data, with solutions deployed on edge hardware in our analyzer platforms. You will work under the supervision of a senior data scientist who will guide your technical development and project execution. We are looking for a curious, adaptable team player eager to build foundational skills in applied machine learning.
What you can expect:
Develop classification and clustering models on tabular data to support hematology analyzer capabilities
Contribute to model development, evaluation, and iteration under the guidance of a senior data scientist
Partner with senior team members to understand requirements, explore data, and validate model performance
Document your work clearly so it can be reviewed, reproduced, and built upon by the team
Deploy your solutions to edge hardware
What you need to succeed:
0-2 years of experience applying machine learning to real-world problems (internships, research, and coursework projects count)
Strong working knowledge of Python and common data science libraries (pandas, scikit-learn, NumPy)
Solid foundation in statistics, machine learning, and algorithms
Demonstrated understanding of classification and clustering methods for tabular data, including when to apply which approach and how to evaluate results
Curiosity about the data and the underlying generating processes - a habit of asking "why" before reaching for a model
A growth mindset and willingness to learn from more senior team members
Ability to communicate analyses and results clearly to your immediate team
Bachelor's degree in a quantitative field (statistics, computer science, math, engineering, or related); advanced degree a plus
Nice to have:
Exposure to deploying ML models on resource-constrained or edge hardware
Familiarity with model optimization techniques (quantization, ONNX, TFLite)
Experience with version control (Git) and collaborative software development practices
Experience modeling data for medical, diagnostic or life sciences applications

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