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F1 Data Science Jobs in Texas (NOW HIRING)

Photon

AI/ML Data Scientist

Dallas, TX · On-site

... F1 score, ROC-AUC, PR-AUC, log loss, calibration, and lift. * Analyze model errors and identify ... Bachelor s or master s degree in Data Science, Statistics, Mathematics, Computer Science ...

New

Photon

AI/ML Data Scientist | Onsite

Dallas, TX · On-site

... F1 score, ROC-AUC, PR-AUC, log loss, calibration, and lift. * Analyze model errors and identify ... Bachelor's or master's degree in Data Science, Statistics, Mathematics, Computer Science ...

Photon

AI/ML Data Scientist | Onsite

Dallas, TX

... F1 score, ROC-AUC, PR-AUC, log loss, calibration, and lift. * Analyze model errors and identify ... Bachelor's or master's degree in Data Science, Statistics, Mathematics, Computer Science ...

McKesson

Data Architect, Databricks

Irving, TX · On-site +1

$61.25 - $78.75/hr

... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ... Bachelor's degree in Computer Science, Information Systems, or a related field, or relative ...

McKesson

Data Architect, Databricks

Irving, TX · On-site +1

$61.25 - $78.75/hr

... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ... Bachelor's degree in Computer Science, Information Systems, or a related field, or relative ...

MCKESSON

Manager, Data Analytics

The Woodlands, TX · On-site

... Science, Data Analytics, or a related field Critical Skills * 7+ years in analytics, business ... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ...

McKesson

Manager, Data Analytics

The Woodlands, TX

... Science, Data Analytics, or a related field Critical Skills * 7+ years in analytics, business ... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ...

McKesson

Manager, Data Analytics

Irving, TX

... Science, Data Analytics, or a related field Critical Skills * 7+ years in analytics, business ... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ...

McKesson

Manager, Data Analytics

Irving, TX

... Science, Data Analytics, or a related field Critical Skills * 7+ years in analytics, business ... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ...

McKesson

Manager, Data Analytics

The Woodlands, TX

... Science, Data Analytics, or a related field Critical Skills * 7+ years in analytics, business ... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ...

MCKESSON

Manager, Data Analytics

Irving, TX · On-site

... Science, Data Analytics, or a related field Critical Skills * 7+ years in analytics, business ... F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide ...

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Showing results 1-20

All JobsF1 Data Science JobsF1 Data Science Jobs in Texas

F1 Data Science information

See Texas salary details

$34.9K

$114.3K

$183.1K

How much do f1 data science jobs pay per year?

As of Aug 7, 2026, the average yearly pay for f1 data science in Texas is $114,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 per year, depending on experience, location, and employer.

Can a data scientist work in F1?

Yes, a data scientist can work in Formula 1 by analyzing race data, vehicle telemetry, and performance metrics to optimize car setup and strategy. F1 teams often seek data scientists skilled in statistical analysis, programming, and machine learning, using tools like Python, R, and SQL to support race performance and development.

What does an F1 Data Science do?

As an F1 Data Science professional, your daily tasks typically include analyzing real-time and historical race data to inform car performance optimization and race strategies. You’ll work closely with engineers, strategists, and drivers to interpret data from sensors and telemetry, build predictive models, and communicate findings to support decision-making. The role often involves developing and maintaining data processing pipelines as well as creating informative visualizations for both technical and non-technical stakeholders. Collaboration is key, as you’ll be part of a multidisciplinary team working together under tight deadlines during race events and testing sessions. This dynamic environment offers exciting opportunities to directly impact race outcomes through your data-driven insights.

How to get into F1 Data Science as a data analyst?

To become an F1 Data Science data analyst, develop strong skills in data analysis, programming (such as Python or R), and statistical modeling. Gaining experience with motorsport data, understanding F1 race operations, and familiarity with data visualization tools like Tableau or Power BI can also be beneficial. A background in engineering, physics, or computer science and relevant internships or projects can improve your chances of entering the field.

What skills and qualifications are needed for an F1 Data Science?

To thrive in an F1 Data Science role, you need a strong background in statistics, data analysis, programming (e.g., Python, R), and a solid understanding of motorsport engineering concepts, typically supported by a relevant degree in data science, engineering, or physics. Proficiency in data visualization tools, machine learning libraries, and race telemetry analysis systems is highly valued. Strong problem-solving ability, attention to detail, and clear communication are essential soft skills, as is a collaborative mindset for working closely with engineers and race strategists. These abilities are important for extracting actionable insights from complex data, optimizing car and team performance, and delivering results in the high-pressure, fast-paced environment of Formula 1.

What is an F1 Data Science?

An F1 Data Science job involves analyzing vast amounts of racing data to optimize car performance, race strategy, and driver decision-making. Data scientists in Formula 1 work with telemetry, simulations, and real-time data to extract insights that improve speed, reliability, and efficiency. They use machine learning, statistical modeling, and engineering principles to enhance car aerodynamics, tire management, and fuel strategy. This role requires a strong background in data analytics, programming (Python, MATLAB, SQL), and a deep understanding of motorsport dynamics.

What are popular job titles related to F1 Data Science jobs in Texas? For F1 Data Science jobs in Texas, the most frequently searched job titles are:
What job categories do people searching F1 Data Science jobs in Texas look for? The top searched job categories for F1 Data Science jobs in Texas are:
What cities in Texas are hiring for F1 Data Science jobs? Cities in Texas with the most F1 Data Science job openings:
F1 Data Science jobs near you
Infographic showing various F1 Data Science job openings in Texas as of July 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 79% In-person, 7% Hybrid, and 14% Remote job distribution, with an average salary of $114,350 per year, or $55 per hour.

AI/ML Data Scientist

Photon

Dallas, TX • On-site

Other

Posted 3 days ago

New

Job description

Role: AI/ML Data Scientist
Location: Dallas, TX (Onsite)
Key Responsibilities
  • Translate business problems into well-defined machine learning and predictive modeling objectives.
  • Collect, clean, transform, and analyze structured and unstructured data from multiple sources.
  • Perform exploratory data analysis to identify trends, relationships, anomalies, biases, and data-quality issues.
  • Develop predictive models from scratch, including data preparation, feature engineering, training, validation, testing, and optimization.
  • Build and apply regression models for forecasting, estimation, risk scoring, pricing, demand prediction, and related use cases.
  • Build and apply classification models for segmentation, fraud detection, churn prediction, recommendation, anomaly detection, and other decision-support applications.
  • Select appropriate algorithms based on the problem type, data characteristics, business requirements, interpretability needs, and operational constraints.
  • Compare baseline, linear, tree-based, ensemble, and other appropriate modeling approaches.
  • Tune model hyperparameters and use appropriate cross-validation strategies to improve generalization.
  • Experience building and deploying AI solutions using Natural Language Processing (NLP), Computer Vision, and sequence modeling techniques for text, image, video, and time-series data.
  • Strong knowledge of deep learning architectures including RNNs, LSTMs, GRUs, CNNs, and Transformer-based models, with hands-on experience using TensorFlow or PyTorch.
  • Ability to evaluate, optimize, and explain AI model performance, including model accuracy, robustness, bias detection, feature interpretation, and production monitoring.
  • Evaluate model performance using relevant metrics such as RMSE, MAE, R , accuracy, precision, recall, F1 score, ROC-AUC, PR-AUC, log loss, calibration, and lift.
  • Analyze model errors and identify opportunities for improving data quality, features, sampling strategies, and model assumptions.
  • Assess model robustness, explainability, fairness, stability, and sensitivity to changing data patterns.
  • Clearly communicate the rationale behind model selection, including why a particular model was chosen over alternatives.
  • Explain technical results, assumptions, limitations, and trade-offs to product managers, business leaders, and other stakeholders.
  • Document analytical methods, data sources, assumptions, experiments, model decisions, and results.
  • Collaborate with data engineers, software engineers, product teams, domain experts, and business stakeholders to operationalize models.
  • Support model deployment, monitoring, retraining, and continuous improvement in production environments.
  • Stay current with developments in machine learning, statistical modeling, AI techniques, and responsible AI practices.
Required Qualifications
  • Bachelor s or master s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative discipline.
  • 3+ years of professional experience in data science, machine learning, predictive analytics, or a closely related field.
  • Strong understanding of statistical analysis, probability, experimental design, and machine learning fundamentals.
  • Demonstrated experience building predictive models from raw data through final evaluation.
  • Deep practical expertise in regression and classification algorithms, including:
  • Linear and polynomial regression
  • Logistic regression
  • Regularization methods such as Ridge, Lasso, and Elastic Net
  • Decision trees
  • Random forests
  • Gradient boosting methods
  • Support vector machines
  • k-nearest neighbors
  • Naive Bayes
  • Ensemble modeling techniques
  • CNN
  • Computervision
  • RNN
  • NLP
  • Strong knowledge of supervised learning workflows, including data splitting, cross-validation, feature selection, feature engineering, model tuning, and evaluation.
  • Proficiency in Python and common data science libraries such as pandas, NumPy, scikit-learn, SciPy, and matplotlib or Seaborn.
  • Strong SQL skills and experience querying, joining, aggregating, and analyzing data from relational databases.
  • Experience working with missing data, outliers, imbalanced classes, categorical variables, high-cardinality features, and data leakage risks.
  • Ability to select and justify appropriate evaluation metrics based on business objectives and model use cases.
  • Experience explaining model behavior using techniques such as feature importance, partial dependence, SHAP, coefficients, permutation importance, or related methods.
  • Excellent written and verbal communication skills.
  • Ability to present complex analytical concepts clearly to audiences with varying levels of technical expertise.
Preferred Qualifications
  • Experience deploying machine learning models through APIs, batch pipelines, or cloud-based platforms.
  • Familiarity with MLflow, Kubeflow, Airflow, Docker, Git, CI/CD, or similar tools.
  • Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
  • Knowledge of time-series forecasting, survival analysis, recommender systems, or anomaly detection.
  • Experience with deep learning frameworks such as PyTorch or TensorFlow.
  • Familiarity with model monitoring, data drift, concept drift, model retraining, and performance degradation.
  • Experience working with distributed data-processing tools such as Spark.
  • Knowledge of responsible AI, model governance, fairness, privacy, and regulatory requirements.
  • Experience working in an Agile or cross-functional product development environment.
Core Competencies
Analytical Thinking
Ability to break down ambiguous problems, identify relevant data, test assumptions, and develop rigorous analytical solutions.
Model Selection and Justification
Ability to explain why a specific model is appropriate based on accuracy, interpretability, scalability, latency, data volume, feature relationships, regulatory requirements, and business impact.
Statistical and Technical Expertise
Strong understanding of statistical concepts and practical machine learning methods, with the ability to distinguish correlation from causation and identify modeling limitations.
Data Understanding
Ability to assess data quality, determine whether variables are meaningful, identify bias and leakage, and understand how data-generating processes affect model results.
Communication
Ability to communicate model assumptions, results, trade-offs, uncertainty, and limitations in clear and accessible language.
Business Orientation
Ability to connect technical modeling outcomes to measurable business goals, operational decisions, customer outcomes, or financial impact.
Collaboration
Ability to work effectively with engineering, product, operations, and leadership teams throughout the model lifecycle.
Expected Modeling Approach
Successful candidates should be able to demonstrate a structured approach that includes:
  1. Defining the business problem and prediction target.
  1. Establishing a simple and interpretable baseline.
  1. Understanding the data-generating process and identifying potential biases.
  1. Performing exploratory data analysis.
  1. Preparing the data and engineering meaningful features.
  1. Selecting candidate models based on the problem and constraints.
  1. Training and validating models using appropriate methodology.
  1. Comparing models using business-relevant metrics.
  1. Explaining model behavior and identifying limitations.
  1. Selecting the final model based on accuracy, interpretability, reliability, and operational fit.
  1. Documenting the decision-making process.
  1. Monitoring and improving the model after deployment.
Deliverables and Success Measures
  • High-quality exploratory analyses that produce actionable insights.
  • Reliable regression and classification models aligned with business objectives.
  • Clearly documented modeling decisions and assumptions.
  • Reproducible data preparation and model-training workflows.
  • Measurable improvements in forecasting accuracy, decision quality, efficiency, revenue, risk reduction, or customer outcomes.
  • Models that are appropriately interpretable, robust, maintainable, and production-ready.
  • Clear communication of model performance, uncertainty, trade-offs, and limitations.
  • Effective collaboration with stakeholders throughout the analytics and model development lifecycle.

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