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Director F1 Data Science Jobs in Colorado (NOW HIRING)

Pwc

AI & GenAI Data Scientist-Director

Denver, CO · On-site

$155K - $410K/yr

Industry/Sector Not Applicable Specialism Data Science Management Level Director & Summary The Opportunity As an AI & GenAI Data Scientist-Director, you will leverage advanced technologies and ...

BillGO, Inc.

AI Data Scientist

Fort Collins, CO · On-site

$102K - $146K/yr

Your models will have a direct impact on financial decisions, operational efficiency, and customer ... Required Qualifications • Bachelor's or Master's degree in Data Science, Computer Science ...

Billgo,-Inc.-1

AI Data Scientist

Fort Collins, CO · On-site

$120 - $180/hr

Your models will have a direct impact on financial decisions, operational efficiency, and customer ... Required QualificationsBachelor's or Master's degree in Data Science, Computer Science, Statistics ...

New

BillGO, Inc.

AI Data Scientist

Fort Collins, CO · On-site

Your models will have a direct impact on financial decisions, operational efficiency, and customer ... Required Qualifications • Bachelor's or Master's degree in Data Science, Computer Science ...

BillGO, Inc.

Data Scientist AI/ML

Fort Collins, CO · On-site

$102K - $146K/yr

Your models will have a direct impact on financial decisions, operational efficiency, and customer ... Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field. * 3+ ...

SPECTRUM

Data Scientist IV

Greenwood Village, CO

$88K/yr

Research and apply knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions  * Develop Machine Learning (ML) models to detect and predict ...

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

Director F1 Data Science information

What does a director F1 data science do?

A Director of F1 Data Science leads a team responsible for analyzing and interpreting complex data related to Formula 1 racing. Their role involves overseeing the development and implementation of data-driven strategies to improve car performance, race strategy, and overall team competitiveness. They collaborate with engineers, data analysts, and race strategists to turn raw data into actionable insights. Additionally, they ensure the use of the latest technologies and methodologies in data science to maintain a competitive edge.

What is the difference between Director F1 Data Science vs Data Scientist?

AspectDirector F1 Data ScienceData Scientist
CredentialsAdvanced degrees (Master's/PhD), leadership experienceBachelor's or Master's in relevant field
Work EnvironmentStrategic leadership, team management, cross-department collaborationData analysis, model development, coding, and experimentation
Employer & Industry UsageAutomotive, motorsport teams, analytics firmsTech companies, finance, healthcare, research institutions

The main difference is that the Director F1 Data Science oversees strategic projects and manages teams within the motorsport industry, while a Data Scientist focuses on hands-on data analysis and model building. The director role requires leadership skills and industry experience, whereas the data scientist role emphasizes technical expertise and coding skills.

What are the key skills and qualifications needed to thrive as a director F1 data science?

To thrive as a Director F1 Data Science, you need advanced expertise in data analytics, machine learning, and statistical modeling, typically backed by a relevant graduate degree and significant experience in motorsport or a similar high-performance environment. Familiarity with tools such as Python, MATLAB, cloud computing platforms, and race data analysis systems is essential, along with a track record of handling large-scale telemetry and simulation data. Strong leadership, strategic thinking, and clear communication are vital soft skills for guiding teams and collaborating with engineers, drivers, and executives. These abilities drive data-driven decision-making and innovation, directly impacting race strategy, car performance, and competitive advantage in Formula 1.

How does a director F1 data science typically collaborate with racing engineers and other technical teams?

A Director of F1 Data Science works closely with racing engineers, aerodynamics specialists, strategists, and software developers to turn complex data into actionable insights. This role involves leading data science projects that help optimize car performance, race strategy, and driver feedback by effectively communicating analytical findings to technical and non-technical stakeholders. Collaboration often includes attending engineering meetings, coordinating data collection during testing, and integrating data solutions into day-to-day team operations to ensure everyone is aligned toward the team’s performance goals.
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Director F1 Data Science jobs near you

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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