Senior Applied Data Science Engineer

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.

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

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