Senior Applied Data Science Engineer
Signature Aviation
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Orlando, FL
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Not Applicable
Posted April 3, 2026
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Responsibilities
Commitments
Responsibilities
- Develop and deploy predictive models supporting revenue forecasting, demand prediction, and operational planning
- Support dynamic pricing initiatives through machine learning models and pricing optimization algorithms
- Design and implement ramp capacity and operational optimization models to improve resource planning and throughput
- Develop data enrichment pipelines that enhance CRM, crew, and operational datasets with predictive insights
- Support enterprise AI platforms through the development of features, embeddings, and model inputs for LLM and RAG-based applications
- Design and implement experimentation frameworks to evaluate model performance and measure business impact
- Build and maintain scalable data pipelines supporting machine learning and analytics workloads
- Integrate predictive analytics and machine learning outputs into enterprise systems and operational workflows
- Maintain strong data governance, quality, and documentation standards across data science assets
Commitments
With more than 225 locations worldwide, Signature Aviation is the largest global network of private aviation terminals, delivering safe, convenient, and elevated experiences to those we serve.
As a premier hospitality organization and a certified Great Place to Work™, we are committed to redefining private air travel.
Not Met Priorities
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Requirements
- 5–8+ years of experience in data science, machine learning, or applied analytics roles
- Demonstrated experience deploying machine learning models into production environments
- Strong foundation in statistics, experimentation design, and predictive modeling
- Experience working with cross-functional teams to translate business problems into data science solutions
- Strong programming skills in Python and SQL
- Experience with data science libraries such as Pandas, NumPy, and scikit-learn
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with vector search, embeddings, and modern retrieval architectures
- Experience with cloud data platforms such as Azure, Snowflake, or Databricks
- Experience with ML lifecycle and experimentation tools such as MLflow
- Familiarity with pricing optimization models, demand forecasting, or revenue intelligence systems
- Experience evaluating or supporting LLM-based systems and AI applications
- Experience in aviation, logistics, transportation, or other operational analytics environments preferred
Preferred Skills
- Strong programming skills in Python and SQL
- Experience with data science libraries such as Pandas, NumPy, and scikit-learn
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with vector search, embeddings, and modern retrieval architectures
- Experience with cloud data platforms such as Azure, Snowflake, or Databricks
- Experience with ML lifecycle and experimentation tools such as MLflow
- Familiarity with pricing optimization models, demand forecasting, or revenue intelligence systems
- Experience evaluating or supporting LLM-based systems and AI applications
- Experience in aviation, logistics, transportation, or other operational analytics environments preferred
Education
- (Not required) – Bachelor’s degree in Data Science, Statistics, Computer Science, or a related quantitative field (advanced degree preferred)
Job Description
At Signature Aviation, we use data and artificial intelligence to optimize operations, improve customer experiences, and drive revenue growth across our global network. The Sr. Engineer, Applied Data Science, will design and deploy predictive models and analytics systems that power key initiatives, including dynamic pricing, revenue forecasting, ramp capacity optimization, CRM data enrichment, and operational automation.
This role partners closely with engineering, product, and operations teams to translate complex data into scalable machine learning solutions and decision intelligence systems that improve business performance and operational efficiency.
Responsibilities
Develop and deploy predictive models supporting revenue forecasting, demand prediction, and operational planning
Support dynamic pricing initiatives through machine learning models and pricing optimization algorithms
Design and implement ramp capacity and operational optimization models to improve resource planning and throughput
Develop data enrichment pipelines that enhance CRM, crew, and operational datasets with predictive insights
Support enterprise AI platforms through the development of features, embeddings, and model inputs for LLM and RAG-based applications
Design and implement experimentation frameworks to evaluate model performance and measure business impact
Build and maintain scalable data pipelines supporting machine learning and analytics workloads
Integrate predictive analytics and machine learning outputs into enterprise systems and operational workflows
Maintain strong data governance, quality, and documentation standards across data science assets
Qualifications
Minimum Education and/or Experience:
5–8+ years of experience in data science, machine learning, or applied analytics roles
Demonstrated experience deploying machine learning models into production environments
Strong foundation in statistics, experimentation design, and predictive modeling
Experience working with cross-functional teams to translate business problems into data science solutions
Bachelor’s degree in Data Science, Statistics, Computer Science, or a related quantitative field (advanced degree preferred)
Additional Knowledge And Skills
Strong programming skills in Python and SQL
Experience with data science libraries such as Pandas, NumPy, and scikit-learn
Experience with deep learning frameworks such as PyTorch or TensorFlow
Familiarity with vector search, embeddings, and modern retrieval architectures
Experience with cloud data platforms such as Azure, Snowflake, or Databricks
Experience with ML lifecycle and experimentation tools such as MLflow
Familiarity with pricing optimization models, demand forecasting, or revenue intelligence systems
Experience evaluating or supporting LLM-based systems and AI applications
Experience in aviation, logistics, transportation, or other operational analytics environments preferred
About Us
With more than 225 locations worldwide, Signature Aviation is the largest global network of private aviation terminals, delivering safe, convenient, and elevated experiences to those we serve. As a premier hospitality organization and a certified Great Place to Work™, we are committed to redefining private air travel. Our nearly 6,000-strong team of aviation experts and enthusiasts is dedicated to delivering excellence to our guests and communities, and it starts with taking care of our team. Signature provides a variety of benefits, programs, and resources to support our team members’ overall well-being and professional development. We proudly volunteer and give back, focusing on elevating the neighborhoods where we operate, empowering the next generation of aviation professionals, and supporting our veterans.
From your health to your financial wellness, there are several benefits for you and your family when joining Signature Aviation.
Our Benefits
Medical/prescription drug, dental, and vision Insurance
Health Savings Account
Flexible Spending Accounts
Life Insurance
Disability Insurance
401(k)
Critical Illness, Hospital Indemnity and Accident Insurance
Identity Theft and Legal Services
Paid time off
Paid Maternity Leave
Tuition reimbursement
Training and Development
Employee Assistance Program (EAP) & Perks
Qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, or other protected characteristics.
At Signature Aviation, we use data and artificial intelligence to optimize operations, improve customer experiences, and drive revenue growth across our global network. The Sr. Engineer, Applied Data Science, will design and deploy predictive models and analytics systems that power key initiatives, including dynamic pricing, revenue forecasting, ramp capacity optimization, CRM data enrichment, and operational automation.
This role partners closely with engineering, product, and operations teams to translate complex data into scalable machine learning solutions and decision intelligence systems that improve business performance and operational efficiency.
Responsibilities
Develop and deploy predictive models supporting revenue forecasting, demand prediction, and operational planning
Support dynamic pricing initiatives through machine learning models and pricing optimization algorithms
Design and implement ramp capacity and operational optimization models to improve resource planning and throughput
Develop data enrichment pipelines that enhance CRM, crew, and operational datasets with predictive insights
Support enterprise AI platforms through the development of features, embeddings, and model inputs for LLM and RAG-based applications
Design and implement experimentation frameworks to evaluate model performance and measure business impact
Build and maintain scalable data pipelines supporting machine learning and analytics workloads
Integrate predictive analytics and machine learning outputs into enterprise systems and operational workflows
Maintain strong data governance, quality, and documentation standards across data science assets
Qualifications
Minimum Education and/or Experience:
5–8+ years of experience in data science, machine learning, or applied analytics roles
Demonstrated experience deploying machine learning models into production environments
Strong foundation in statistics, experimentation design, and predictive modeling
Experience working with cross-functional teams to translate business problems into data science solutions
Bachelor’s degree in Data Science, Statistics, Computer Science, or a related quantitative field (advanced degree preferred)
Additional Knowledge And Skills
Strong programming skills in Python and SQL
Experience with data science libraries such as Pandas, NumPy, and scikit-learn
Experience with deep learning frameworks such as PyTorch or TensorFlow
Familiarity with vector search, embeddings, and modern retrieval architectures
Experience with cloud data platforms such as Azure, Snowflake, or Databricks
Experience with ML lifecycle and experimentation tools such as MLflow
Familiarity with pricing optimization models, demand forecasting, or revenue intelligence systems
Experience evaluating or supporting LLM-based systems and AI applications
Experience in aviation, logistics, transportation, or other operational analytics environments preferred
About Us
With more than 225 locations worldwide, Signature Aviation is the largest global network of private aviation terminals, delivering safe, convenient, and elevated experiences to those we serve. As a premier hospitality organization and a certified Great Place to Work™, we are committed to redefining private air travel. Our nearly 6,000-strong team of aviation experts and enthusiasts is dedicated to delivering excellence to our guests and communities, and it starts with taking care of our team. Signature provides a variety of benefits, programs, and resources to support our team members’ overall well-being and professional development. We proudly volunteer and give back, focusing on elevating the neighborhoods where we operate, empowering the next generation of aviation professionals, and supporting our veterans.
From your health to your financial wellness, there are several benefits for you and your family when joining Signature Aviation.
Our Benefits
Medical/prescription drug, dental, and vision Insurance
Health Savings Account
Flexible Spending Accounts
Life Insurance
Disability Insurance
401(k)
Critical Illness, Hospital Indemnity and Accident Insurance
Identity Theft and Legal Services
Paid time off
Paid Maternity Leave
Tuition reimbursement
Training and Development
Employee Assistance Program (EAP) & Perks
Qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, or other protected characteristics.