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Apprentice Machine Learning Testing Jobs in South Carolina

Sonoco

Machinist (Apprentice)

Hartsville, SC · On-site

$27.40/hr

This role is perfect for those who embrace problem-solving, enjoy hands-on learning, and are ... Thorough knowledge of machine shop equipment and have at least 5 years' experience working with ...

Langford

Apprentice

Greer, SC · On-site

... testers, and drills. They learn to safely and effectively use these tools to perform tasks and ... Continuous Learning: As apprentices progress in their training, they are expected to actively learn ...

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Apprentice Machine Learning Testing information

What does an apprentice machine learning testing do?

An Apprentice Machine Learning Testing professional assists in evaluating and validating machine learning models to ensure they perform as expected. They typically work under the guidance of experienced data scientists or engineers, running tests, analyzing results, and helping to identify issues such as bias or inaccuracies in algorithms. Their responsibilities may also include developing test cases, writing reports, and learning about data preprocessing and evaluation metrics. This role is ideal for those who are new to the field and want to build foundational skills in machine learning quality assurance.

What kinds of projects or tasks can I expect to work on as an apprentice machine learning testing?

As an Apprentice Machine Learning Testing, you’ll typically assist in evaluating machine learning models by designing and running tests, analyzing model outputs, and helping identify issues like bias or overfitting. You may work closely with data scientists and software engineers to validate model performance and ensure results align with project objectives. Your daily tasks might include preparing test datasets, executing automated testing scripts, and documenting findings to help improve model reliability. This role often serves as a valuable introduction to practical machine learning workflows and quality assurance processes in technical teams.

What are the key skills and qualifications needed to thrive as an apprentice machine learning testing, and why are they important?

To thrive as an Apprentice in Machine Learning Testing, a foundational understanding of statistics, programming (especially Python), and basic machine learning concepts is essential, often supported by a degree or coursework in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help apprentices collaborate and identify testing issues efficiently. These skills ensure accurate model validation, effective troubleshooting, and contribute to the robust deployment of machine learning solutions.

What is the difference between Apprentice Machine Learning Testing vs Machine Learning Engineer?

AspectApprentice Machine Learning TestingMachine Learning Engineer
Required CredentialsBasic understanding of ML concepts, often pursuing relevant certifications or degreesAdvanced degrees (BSc, MSc, PhD) in CS or related fields, with extensive experience
Work EnvironmentEntry-level, supervised testing environments, often in training programsFull-time, independent development and deployment of ML models in production
Employer & Industry UsageInternships, training programs, entry-level roles in tech companiesEstablished tech firms, startups, research institutions

Apprentice Machine Learning Testing roles focus on learning and assisting with testing ML models under supervision, while Machine Learning Engineers design, build, and deploy ML systems independently. The apprentice position is ideal for gaining foundational skills, whereas the engineer role requires advanced expertise and experience.

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For Apprentice Machine Learning Testing jobs in South Carolina, the most frequently searched job titles are:

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The top searched job categories for Apprentice Machine Learning Testing jobs in South Carolina are:

What cities in South Carolina are hiring for Apprentice Machine Learning Testing jobs?

Cities in South Carolina with the most Apprentice Machine Learning Testing job openings:

Apprentice Machine Learning Testing jobs near you
Infographic showing various Apprentice Machine Learning Testing job openings in South Carolina as of June 2026, with employment types broken down into 2% Internship, 1% As Needed, 47% Full Time, 34% Part Time, 11% Temporary, and 5% Nights. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution.

Principal Machine Learning Scientist

Irmo, SC • On-site

DIESEL LAPTOPS LLC
Motor Vehicle Manufacturing • 11 - 50 employees
Learn more about DIESEL LAPTOPS LLCexternal

$118 - $126/hr

Other

Posted 20 days ago

Job description

Job Details
  • Job Location: Remote - CO - Remote, CO 80210
  • Position Type: Full Time
  • Education Level: Graduate Degree
  • Salary Range: $118.00 - $126.00
  • SalaryTravel Percentage: Negligible
  • Job Title: Principal Data Scientist, Vehicle Analytics
Company Overview

Diesel Laptops is a leading provider of diagnostic tools, repair information, software, training, and technology solutions for the commercial truck and off-highway vehicle repair industry. We help repair facilities, fleets, technicians, and industry partners reduce downtime, improve repair accuracy, and make better operational decisions.

Position Summary

The Principal Data Scientist, Vehicle Analytics, is a senior individual contributor responsible for solving complex business, vehicle, and engineering problems through statistical analysis, experimentation, predictive modeling, and applied data science.

This role partners closely with Software Engineering, Data Engineering, Product, Remote Solutions Engineering, and customer-facing teams to identify high-value problems, define analytical approaches, validate hypotheses, and deploy reliable data-driven capabilities.

Machine learning is an important part of the role, but success is defined by selecting the most appropriate analytical method for each problem rather than applying machine learning where a simpler statistical or analytical approach would be more reliable, explainable, or useful.

This position does not have routine direct reports but is expected to provide scientific leadership, technical mentorship, and guidance across the organization.

Key ResponsibilitiesData Analysis and Scientific Problem Solving
  • Investigate complex business, vehicle, and engineering problems using exploratory data analysis, statistical analysis, experimentation, and hypothesis testing.
  • Analyze vehicle telemetry, time-series data, fault codes, service history, repair outcomes, and operational data.
  • Translate ambiguous customer and business questions into measurable hypotheses, analytical plans, and actionable recommendations.
  • Identify trends, anomalies, failure patterns, and operational drivers that affect vehicle reliability, maintenance, and customer outcomes.
  • Present findings, limitations, uncertainty, and recommendations to technical and nontechnical stakeholders.
Statistical Modeling and Machine Learning
  • Design, develop, validate, and improve statistical models, anomaly-detection methods, predictive-maintenance models, classification systems, and related analytical solutions.
  • Determine whether statistical analysis, machine learning, experimentation, or another analytical method is most appropriate for the problem.
  • Define and monitor performance measures such as precision, recall, F1 score, false-positive rate, stability, latency, and business impact.
  • Document assumptions, methodology, validation results, limitations, and performance findings to ensure reproducibility and transparency.
  • Monitor deployed models and analyses and recommend retraining, redesign, or retirement when appropriate.
Data Products and Production Systems
  • Build production-ready analytical workflows, contextual tools, reports, prototypes, and model components.
  • Partner with Data Engineering and Software Engineering to productionize analyses and models using reliable pipelines, APIs, testing, observability, and deployment practices.
  • Contribute code and technical documentation using approved engineering standards.
  • Support testing, validation, monitoring, and continuous improvement of production data-science solutions.
  • Ensure analytical work is auditable, reproducible, maintainable, and appropriately documented.
Collaboration and Technical Leadership
  • Partner with Product, Engineering, Remote Solutions Engineering, Customer Success, and business leaders to identify and prioritize analytical opportunities.
  • Participate in technical design discussions, scientific reviews, code reviews, and model-validation reviews.
  • Mentor data scientists, analysts, and engineers in statistics, experimentation, analytical reasoning, and model evaluation.
  • Establish and promote best practices for analytical quality, reproducibility, documentation, and responsible model use.
  • Communicate scientific findings and recommendations to executives, customers, and other stakeholders when required.
QualificationsRequired
  • Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, or a related quantitative field, or equivalent advanced professional experience.
  • Seven or more years of progressive experience in applied data science, statistical modeling, machine learning, or quantitative research.
  • Demonstrated experience solving complex problems using large, imperfect, high-volume, or time-series datasets.
  • Advanced proficiency with Python and SQL.
  • Strong experience with Pandas, NumPy, SciPy, and Jupyter.
  • Strong foundation in statistics, experimental design, hypothesis testing, model validation, and communication of uncertainty.
  • Experience developing and deploying production data-science or machine-learning solutions.
  • Ability to independently define methodology, evaluate technical tradeoffs, and lead complex analytical initiatives.
  • Strong written and verbal communication skills.
Preferred Qualifications
  • PhD in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
  • Experience with vehicle telemetry, IoT, connected-device, fleet, transportation, predictive-maintenance, or industrial time-series data.
  • Experience with anomaly detection, equipment-failure prediction, maintenance optimization, natural-language processing, or large language models.
  • Experience with dbt, Dagster, Apache Flink, ClickHouse, PostgreSQL, Apache Iceberg, Docker, and MLflow.
  • Experience producing statistically valid customer-facing analyses, technical case studies, or research reports.
  • Publication, patent, or significant applied-research experience.
Core Technologies
  • Python
  • SQL
  • Pandas
  • NumPy
  • SciPy
  • Jupyter Notebooks
  • dbt
  • Dagster
  • Apache Flink
  • ClickHouse
  • PostgreSQL
  • Apache Iceberg
  • Git
  • GitHub
  • Docker
  • MLflow
Position Details
  • Full-time
  • Exempt
  • Principal individual-contributor role
  • Remote within the United States, with hybrid eligibility in Columbia, South Carolina
  • Occasional travel may be required
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