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Applied Machine Learning Intern Jobs in Durham, NC

Jobtailor

Data Scientist

Raleigh, NC · On-site

$110 - $170/hr

... in applied statistical modeling and machine learning, with a track record of deployed production models. * Strong programming skills in Python (PySpark, pandas, NumPy, scikit-learn, statsmodels ...

New

North Carolina State University

LAS Intern

Raleigh, NC · On-site

$14.50 - $19.50/hr

Potential areas of research include but are not limited to: social sciences, applied sciences and ... LAS Intern Requirements and Preferences Work Schedule - TBD with Supervisor Other Work ...

McGough Construction

Manager, Data Science

Raleigh, NC · On-site

... applied modeling * Proven experience building statistical or machine learning models and delivering them in real-world business contexts with measurable outcomes * Strong programming experience in ...

Deloitte

Lead AI and Data Science Engineer II

Raleigh, NC · On-site

$99K - $131K/yr

Data science applies statistical analysis, machine learning, and AI to identify meaningful patterns ... Graduate degree in Applied Statistics, Computer Science, Life Sciences, Industrial-Organizational ...

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Showing results 21-40

All JobsApplied Machine Learning Intern JobsApplied Machine Learning Intern Jobs in Durham, NC

Applied Machine Learning Intern information

See Durham, NC salary details

$24.6K

$41.1K

$85K

How much do applied machine learning intern jobs pay per year?

As of Aug 8, 2026, the average yearly pay for applied machine learning intern in Durham, NC is $41,149.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,400.00 and $44,400.00 per year, depending on experience, location, and employer.

What is the difference between Applied Machine Learning Intern vs Data Science Intern?

AspectApplied Machine Learning InternData Science Intern
Required SkillsMachine learning algorithms, programming (Python, R), data analysisStatistical analysis, data visualization, programming (Python, R)
Work EnvironmentDeveloping ML models, experimenting with algorithms, deploying modelsData cleaning, analysis, reporting insights
Industry UsageTech companies, AI startups, research labsBusiness analytics, market research, finance

Applied Machine Learning Interns focus on developing and deploying machine learning models, requiring knowledge of algorithms and programming. Data Science Interns typically handle data analysis, visualization, and reporting. While both roles involve data skills, applied ML interns work more on model implementation, whereas data science interns focus on insights and data interpretation.

What are popular job titles related to Applied Machine Learning Intern jobs in Durham, NC? For Applied Machine Learning Intern jobs in Durham, NC, the most frequently searched job titles are:
What job categories do people searching Applied Machine Learning Intern jobs in Durham, NC look for? The top searched job categories for Applied Machine Learning Intern jobs in Durham, NC are:
What cities near Durham, NC are hiring for Applied Machine Learning Intern jobs? Cities near Durham, NC with the most Applied Machine Learning Intern job openings:
Applied Machine Learning Intern jobs near you

Data Scientist

Jobtailor

Raleigh, NC • On-site

$110 - $170/hr

Other

Posted 3 days ago

New

Job description

  • Design and implement statistical and machine learning models for time-series forecasting, anomaly detection, and asset health scoring across utility networks.
  • Build and maintain end-to-end ML pipelines on Databricks from feature engineering and model training to validation, deployment, and monitoring in production.
  • Apply classical statistical methods (GLMs, GAMs, mixed-effects models, Bayesian inference) alongside modern ML techniques (ensemble approaches, network analysis, neural networks) to solve grid operations problems.
  • Develop predictive maintenance and degradation models for utility infrastructure using telemetry and SCADA data at scale.
  • Translate ambiguous business problems into well-defined modeling problems with appropriate statistical frameworks - e.g., knowing when a LM/GLM is sufficient and when gradient boosting or deep learning is warranted.
  • Implement model monitoring, drift detection, and automated retraining workflows to maintain model performance over time.
  • Contribute to load forecasting, demand response optimization, and outage prediction systems.
  • Ensure model interpretability and explainability for utility stakeholders and regulatory compliance.
  • Contribute to internal knowledge-sharing on statistical best practices.
Requirements
  • Bachelor’s degree or equivalent in Statistics, Applied Mathematics, Physics, Engineering, Data Science, or a related quantitative field.
  • 3+ years of experience (with Bachelor’s), 2+ years of experience (with Masters), or 1+ years (with PhD) in applied statistical modeling and machine learning, with a track record of deployed production models.
  • Strong programming skills in Python (PySpark, pandas, NumPy, scikit-learn, statsmodels, XGBoost), R (tidyverse, lme4, glmmTMB, glmnet, mgcv), and SQL for large-scale data analysis.
  • Experience with time-series modeling (ARIMA, state-space models, LSTM, Darts, or similar) on high-volume meter data.
  • Exposure to Databricks ML ecosystem (Feature Store, Experiment Track, Model Serving, Mosaic AI) and MLflow.
  • Familiarity with distributed computing concepts - PySpark, Optuna/Ray, Spark SQL, partitioning strategies, and medallion architecture.
  • Understanding of software engineering principles - version control (Git), testing, CI/CD for ML systems.
  • Ability to communicate complex statistical/ML concepts to non-technical stakeholders.
Core Competencies

Demonstrates expertise in statistical modeling and machine learning for time-series forecasting and anomaly detection, with a strong focus on building and maintaining ML pipelines and ensuring model performance and interpretability. Proficient in translating business problems into statistical frameworks and communicating complex concepts to stakeholders.

Highest-signal resume keywords
  • Statistical Modeling
  • Machine Learning
  • Python Programming
  • Time-Series Modeling
  • Databricks ML Ecosystem
ATS Optimization KeywordsHard Skills
  • Statistical Methods
  • Machine Learning Techniques
  • Feature Engineering
  • Model Training
  • Model Validation
  • Model Deployment
  • Model Monitoring
  • Anomaly Detection
  • Predictive Maintenance
  • Data Analysis
Soft Skills
  • Communication
  • Problem-Solving
Industry Keywords
  • Utility Networks
  • Telemetry Data
  • SCADA Data
  • Regulatory Compliance
  • Grid Operations
Tools & Technologies
  • Databricks
  • PySpark
  • SQL
  • MLflow
  • Git
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