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Internship F1 Data Science Jobs in Colorado (NOW HIRING)

Medtronic

Engineering Intern - Summer 2027

Boulder, CO

$17.25 - $22.50/hr

... internship positions. Individuals on temporary visa classifications, including E, F1 (CPT or OPT ... Experience with engineering software, programming, CAD, data analysis, or laboratory equipment.

Medtronic

Engineering Intern - Summer 2027

Lafayette, CO

$17.50 - $22.75/hr

... internship positions. Individuals on temporary visa classifications, including E, F1 (CPT or OPT ... Experience with engineering software, programming, CAD, data analysis, or laboratory equipment.

Medtronic

Engineering Intern - Summer 2027

Boulder, CO

$17.25 - $22.50/hr

... internship positions. Individuals on temporary visa classifications, including E, F1 (CPT or OPT ... Experience with engineering software, programming, CAD, data analysis, or laboratory equipment.

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All JobsF1 Data Science JobsInternship F1 Data Science JobsInternship F1 Data Science Jobs in Colorado

Internship F1 Data Science information

What is the difference between Internship F1 Data Science vs Data Analyst Intern?

AspectInternship F1 Data ScienceData Analyst Intern
Required CredentialsRelevant coursework, basic programming skills, possibly some certificationsSimilar: coursework, basic analytics skills, some certifications
Work EnvironmentTech companies, finance, healthcare; collaborative, project-basedBusiness, tech, consulting; data-focused, team-oriented
Employer & Industry UsageInternships in data science teams across industriesInternships in analytics teams across industries
Comparison Search IntentYesYes

Internship F1 Data Science and Data Analyst Intern roles share similar requirements and work environments, focusing on data handling and analysis. However, Data Science internships often emphasize machine learning, statistical modeling, and programming, while Data Analyst internships focus more on data visualization, reporting, and basic analytics. Both roles serve as entry points into data careers, but their specific skill sets and project types differ slightly.

What types of projects can an intern expect to work on during an F1 Data Science internship?

As an F1 Data Science intern, you can expect to work on projects involving race data analysis, predictive modeling, and performance optimization. You'll collaborate with engineering and analytics teams to process telemetry data, build machine learning models, and generate insights that can influence race strategies. Interns often have the opportunity to contribute to real-world decisions by visualizing data or automating data pipelines. This experience provides a hands-on understanding of how data science directly impacts the competitive edge in Formula 1.

What is an Internship F1 Data Science?

An Internship F1 Data Science is a temporary, entry-level position typically offered to students or recent graduates who are interested in applying data science techniques to Formula 1 (F1) motorsport. Interns in this role work with large sets of racing data, assist with data analysis, and contribute to performance optimization for F1 teams. They gain hands-on experience with real-world data, advanced analytics, and machine learning models, often working alongside experienced data scientists and engineers. This internship provides valuable exposure to both the fast-paced F1 environment and the technical demands of sports analytics.

What are the key skills and qualifications needed to thrive as an Internship F1 Data Science?

To thrive as an F1 Data Science intern, you need a solid background in statistics, mathematics, and programming (often with Python or R), supported by ongoing or completed studies in a STEM field. Familiarity with data analysis tools, machine learning libraries, and motorsport telemetry systems is typically expected. Strong problem-solving abilities, attention to detail, and effective communication skills help interns contribute meaningfully to team projects. These capabilities are crucial for interpreting complex data, supporting performance optimization, and collaborating in the high-pressure, fast-paced environment of F1 teams.
What are the most commonly searched types of F1 Data Science jobs in Colorado? The most popular types of F1 Data Science jobs in Colorado are:
What are popular job titles related to Internship F1 Data Science jobs in Colorado? For Internship F1 Data Science jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Internship F1 Data Science jobs in Colorado look for? The top searched job categories for Internship F1 Data Science jobs in Colorado are:
Internship F1 Data Science jobs near you
Infographic showing various Internship F1 Data Science job openings in Colorado as of August 2026, with employment types broken down into 60% Internship, 20% Part Time, and 20% Temporary. Highlights an 100% In-person job distribution.

Senior Data Scientist

R2 Technologies Corporation

Denver, CO • On-site

Full-time

Re-posted 28 days ago

Job description

Overview:
Job Title: Senior Data Scientist - Knowledge Domain: Product (Job ID: 2099)
Location: Work From Home - USA, Denver, Colorado 80237 - look for locals
Duration: July 15, 2025 - February 27, 2026
Company: Western Union
Hire Type: Contractor (Contract Only)
Standard Hours per Week: 40
JOB DESCRIPTION
Senior Data Scientist - Knowledge Domain: Product
We are seeking a technically advanced and product-oriented Senior Data Scientist to lead the development of machine learning and deep learning solutions that power intelligent decision-making and innovative products. This role is ideal for someone with extensive experience in building, evaluating, and deploying ML and neural network models in production environments. You'll collaborate cross-functionally to create and scale real-world AI applications that have direct impact on users and business performance.
Role Responsibilities:
Design, build, and evaluate machine learning and deep learning models for classification, regression, recommendation, NLP, computer vision, and time-series forecasting.
Apply deep learning techniques (e.g., CNNs, RNNs, LSTMs, Transformers) to solve complex, data-intensive problems.
Lead the development of ML products, from model prototyping through production deployment, performance monitoring, and continuous improvement.
Select appropriate architectures and hyperparameters, optimize model performance, and use proper evaluation metrics (e.g., AUC, F1, BLEU, IoU, perplexity) based on the use case.
Collaborate with product managers and engineers to translate business challenges into deployable solutions using AI/ML.
Design automated pipelines for data preprocessing, feature engineering, training, and inference (batch or real-time).
Evaluate model drift, monitor performance post-deployment, and implement retraining pipelines as part of a production MLOps system.
Mentor junior data scientists, contribute to code reviews, and lead technical discussions across the data science and engineering teams.
Role Requirements:
Bachelor's degree in Computer Science, Statistics, Applied Math, or related field (Master's or PhD strongly preferred).
5+ years of industry experience in applied machine learning, with 2+ years focused on deep learning and neural network applications.
Experience in Banking, Payments or Financial Services formulating AI data solutions that allow us to leverage our data to know our customers better and target our resources for better market penetration and focused attention and education.
Proficiency in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, Keras, or PyTorch.
Deep understanding of neural networks, model regularization, overfitting/underfitting prevention, and GPU-accelerated training.
Experience with customer data enrichments.
Proven track record of building, evaluating, and deploying machine learning models at scale in production environments.
Experience with cloud platforms (AWS/GCP/Azure), containerization, and model serving technologies.
Excellent communication skills, with the ability to present complex findings to both technical and non-technical stakeholders.
Hands-on experience with real-world applications of deep learning, such as recommendation engines, fraud detection, customer segmentation, document summarization, image recognition, or speech processing.
Familiarity with MLOps tools (e.g., MLflow, SageMaker, Airflow, Kubeflow).
Experience with CI/CD for ML, feature stores, and real-time inference systems.
Contributions to academic research, open-source ML projects, or ML/AI patents.
Skills:
Knowledge Domain

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