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Statistical Learning Jobs in Long Beach, CA (NOW HIRING)

MiniMed

Principal Biomedical Engineer

Los Angeles, CA · On-site

Applies analytical tools, including statistical learning methods where appropriate, to support defect detection, classification, and sensor process monitoring improvements. * Utilizes technical ...

Ingrammicro

Senior Agentic AI Engineer

Irvine, CA · On-site

$112K - $154K/yr

Continuous Learning & Evangelism: Stay at the forefront of advancements in agentic AI, LLMs, and statistical learning theory, and act as a subject matter expert, evangelizing new analytical ...

Ingram Micro

Senior Agentic AI Engineer

Irvine, CA · On-site

$112K - $154K/yr

Continuous Learning & Evangelism: Stay at the forefront of advancements in agentic AI, LLMs, and statistical learning theory, and act as a subject matter expert, evangelizing new analytical ...

General Matter

Statistician

Los Angeles, CA · On-site

Responsibilities : • Perform basic statistical calculations to support inventory checks and ... in learning about nuclear safeguards and regulatory compliance. Company : General Matter is a ...

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All JobsStatistical Learning JobsStatistical Learning Jobs in Long Beach, CA

Statistical Learning information

What are the key skills and qualifications needed to thrive as a statistical learning specialist?

To thrive as a Statistical Learning Specialist, you need a strong background in statistics, probability, and machine learning, typically supported by an advanced degree in statistics, mathematics, computer science, or a related field. Expertise with programming languages such as Python or R, experience with statistical software (e.g., SAS, MATLAB), and familiarity with data analysis libraries are essential. Critical thinking, problem-solving, and effective communication skills help translate complex data insights into actionable business strategies. These competencies are crucial for extracting meaningful patterns from data and driving data-informed decision-making.

How do professionals in statistical learning typically collaborate with data scientists and domain experts on projects?

Professionals in statistical learning often work closely with data scientists and domain experts to ensure that the models they develop are both statistically sound and practically relevant. Collaboration usually involves joint problem definition, sharing data insights, and iterative feedback on model performance. Statistical learning experts contribute their knowledge of algorithms and statistical methods, while data scientists handle data pre-processing and engineering, and domain experts provide context to interpret results. This multidisciplinary teamwork helps ensure that solutions are robust and actionable for stakeholders.

What is the difference between Statistical Learning vs Data Analyst?

AspectStatistical LearningData Analyst
Required CredentialsDegree in Statistics, Data Science, or related fieldsDegree in Statistics, Data Science, Business, or related fields
Work EnvironmentResearch, academia, tech companies, data science teamsBusiness, marketing, finance, healthcare organizations
Employer & Industry UsageTech firms, research institutions, startupsCorporations, consulting firms, government agencies
Common Search & ComparisonStatistical Learning vs Data Analyst

Statistical Learning focuses on developing models and algorithms to understand data patterns, often requiring advanced statistical and programming skills. Data Analysts interpret data to generate reports and insights, typically emphasizing data visualization and business understanding. While both roles analyze data, Statistical Learning is more research-oriented and technical, whereas Data Analysts focus on practical data interpretation for decision-making.

What will I become if I study statistical learning?

Studying statistical learning can lead to roles such as data scientist, data analyst, machine learning engineer, or statistician. These positions involve analyzing data, building predictive models, and applying statistical methods using tools like R or Python in various industries.

What are popular job titles related to Statistical Learning jobs in Long Beach, CA?

For Statistical Learning jobs in Long Beach, CA, the most frequently searched job titles are:

What job categories do people searching Statistical Learning jobs in Long Beach, CA look for?

The top searched job categories for Statistical Learning jobs in Long Beach, CA are:

What cities near Long Beach, CA are hiring for Statistical Learning jobs?

Cities near Long Beach, CA with the most Statistical Learning job openings:

Statistical Learning jobs near you
Infographic showing various Statistical Learning job openings in Long Beach, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

AI Research Assistant - Machine Learning & AI

StudyFetch

Beverly Hills, CA • On-site, Remote

$35 - $50/hr

Part-time

Posted 14 days ago

Job description

About Studyfetch
StudyFetch is the #1 AI-native learning platform globally, transforming how millions of students learn through personalized AI-powered education. We're growing fast with backing from top-tier investors and a mission that's redefining the future of education and ethical learning.
About the Role
We are looking for a highly motivated Master's student in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field to join our AI research team on a part-time basis.
Our research focuses primarily on the intersection of artificial intelligence and education, including the development and evaluation of AI systems that can improve teaching and learning. You will work directly with researchers and engineers on projects involving machine learning, generative AI, large language models, multimodal AI, educational data, model evaluation, and AI systems.
This is a hands-on research position. You will not simply be assisting with administrative research tasks-you will be expected to read papers, implement ideas, run experiments, analyze results, and contribute to the development of new research directions.
Depending on your interests and experience, your work may include developing and evaluating models, building research datasets, designing benchmarks and evaluation methodologies, reproducing published research, fine-tuning open-source models, analyzing educational data, and investigating novel applications of AI in education.
You will also contribute to the broader research development process, including identifying relevant research opportunities, analyzing RFPs and funding opportunities, and assisting with grant and research proposal development.
What You'll Do
  • Read and analyze recent research papers in machine learning, AI, and AI in education.
  • Implement and reproduce methods from recent research.
  • Design and conduct controlled experiments and ablation studies.
  • Train, fine-tune, and evaluate machine learning models.
  • Develop datasets and data-processing pipelines for AI research.
  • Build and maintain evaluation and benchmarking systems.
  • Analyze model performance and experimental results.
  • Investigate new approaches to improving AI capabilities, reliability, and effectiveness in educational settings.
  • Develop research prototypes in Python and modern ML frameworks.
  • Analyze educational datasets and student interaction data to identify research opportunities and patterns.
  • Document experiments, findings, and methodologies.
  • Collaborate with researchers and engineers to refine research hypotheses.
  • Contribute to technical reports, research papers, presentations, and potentially open-source projects.
  • Research and analyze RFPs, grant opportunities, and government or foundation funding programs relevant to AI and education.
  • Assist with the development of grant proposals, research proposals, technical narratives, and supporting materials.
  • Help identify research questions and proposed technical approaches that align with funding opportunities.
  • Track relevant developments in AI research, education technology, and government research priorities.

Areas of Research
Our research primarily focuses on the application and development of AI for education. Projects may span several areas, including:
  • Large Language Models (LLMs)
  • Generative AI
  • Multimodal AI
  • Computer vision
  • Natural language processing
  • AI tutoring and educational agents
  • Personalized learning
  • Student modeling and learning analytics
  • Model training and fine-tuning
  • Reinforcement learning and post-training
  • AI evaluation and benchmarking
  • Synthetic data generation
  • Representation learning
  • Retrieval-augmented generation (RAG)
  • Educational datasets and data infrastructure
  • AI safety, reliability, and evaluation in education

You do not need experience in all of these areas. Depth in one area, strong research fundamentals, and demonstrated ability to learn quickly are more important than breadth.
Required Qualifications
  • Currently pursuing or recently completed a Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, Electrical Engineering, or a closely related field.
  • Strong foundation in machine learning and deep learning.
  • Strong Python programming skills.
  • Experience with at least one modern ML framework, preferably PyTorch.
  • Familiarity with fundamental concepts in neural networks, optimization, and statistical learning.
  • Ability to read and understand technical research papers.
  • Experience conducting technical or academic projects involving machine learning.
  • Strong analytical and problem-solving skills.
  • Strong written communication skills and the ability to clearly explain technical concepts.
  • Ability to work independently while collaborating closely with a research team.
  • Genuine interest in the application of AI to education.
Preferred Qualifications
  • Experience with LLMs, Transformers, multimodal models, computer vision, NLP, reinforcement learning, or generative AI.
  • Experience fine-tuning or training machine learning models.
  • Experience working with GPU-based ML environments.
  • Familiarity with Hugging Face Transformers or similar ML ecosystems.
  • Experience designing datasets, benchmarks, or model evaluations.
  • Experience reproducing results from published research.
  • Research experience through a Master's thesis, research lab, internship, or independent project.
  • Experience working with educational, student, or learning data.
  • Experience with AI evaluation, benchmarking, or experimental design.
  • Publications, preprints, conference submissions, or other demonstrated research output.
  • Contributions to open-source ML/AI projects.
  • Familiarity with Linux, Git, Docker, or distributed computing.
  • Experience researching or writing grant proposals, RFP responses, technical proposals, or research funding applications.
  • Familiarity with federal, state, foundation, or other research funding programs.

What We're Looking For
We're particularly interested in people who are curious, technically rigorous, and excited by the process of discovering something that doesn't already have an obvious answer.
The strongest candidates will be able to demonstrate that they have gone beyond simply completing coursework-for example, by:
  • Building and training their own models.
  • Reproducing a research paper.
  • Conducting an independent ML research project.
  • Developing a substantial Master's thesis.
  • Creating an interesting dataset or benchmark.
  • Investigating why a model succeeds or fails.
  • Working with real-world educational or student data.
  • Publishing or presenting research.
  • Contributing to an open-source ML project.
  • Writing or contributing to a research proposal or grant application.

You do not need to have published a paper to be successful in this role. We care more about your ability to think scientifically, write good code, design meaningful experiments, communicate clearly, and learn quickly.
We also value candidates who are interested in the broader research process-not just model development-including identifying research opportunities, understanding funding priorities, analyzing RFPs, and helping translate technical ideas into compelling research proposals.
Position Details
  • Position: Part-Time AI Research Assistant
  • Focus: Machine Learning & AI for Education
  • Hours: Approximately 15-25 hours per week
  • Location: [Remote / Hybrid]
  • Schedule: Flexible schedule designed to accommodate graduate coursework and research commitments
  • Compensation: $35-50/hour, depending on experience and qualifications

This position is designed for a graduate student who wants meaningful, hands-on experience conducting applied AI research in an industry research environment, with opportunities to contribute to research publications, funded research initiatives, datasets, benchmarks, and real-world AI systems.
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