F
Lead AI Engineer
ForeFlight
Onsite (Austin, TX)
Senior Level
Posted 1 week ago
Skills
Machine Learning
Python
R
SQL
Data Science
Statistical Modeling
GenAI
NLP
OCR
Databricks
XGBoost
Scikit-learn
Tidymodels
Feature Engineering
CI/CD
Mentorship
About the Role
Jeppesen ForeFlight is hiring a Lead AI Engineer to join our RADAR (Reporting, Analytics, Data, AI
& Research) team. This is the team’s first strategic ML engineering hire, and you’ll play a
foundational role in shaping how we apply reproducible statistical programming, analytics
automation, and GenAI to solve real business problems across Finance, Customer Success,
Revenue Operations, Accounting, and Product.
& Research) team. This is the team’s first strategic ML engineering hire, and you’ll play a
foundational role in shaping how we apply reproducible statistical programming, analytics
automation, and GenAI to solve real business problems across Finance, Customer Success,
Revenue Operations, Accounting, and Product.
You will design and build end-to-end machine learning pipelines, extract information from structured
and unstructured sources (PDFs, disparate systems, scanned documents), and serve as a technical
mentor who elevates the analytical capabilities of the broader team. This role blends deep applied
statistics with modern analytics and ML engineering practices. We’re looking for someone who can
move fluidly between exploratory analysis and SQL deep dives, production-grade modeling, and
teaching others how to do the same.
and unstructured sources (PDFs, disparate systems, scanned documents), and serve as a technical
mentor who elevates the analytical capabilities of the broader team. This role blends deep applied
statistics with modern analytics and ML engineering practices. We’re looking for someone who can
move fluidly between exploratory analysis and SQL deep dives, production-grade modeling, and
teaching others how to do the same.
Key Responsibilities
• Design, build, and maintain reproducible end-to-end machine learning pipelines for
multivariate regression and classification tasks using frameworks such as R’s tidymodels
and/or Python’s scikit-learn.
• Apply gradient boosting methods (XGBoost, LightGBM) and ensemble approaches (random
forests), among other ML and deep learning algorithms, to high-impact business problems.
• Implement rigorous data pre-processing, feature engineering, hyperparameter optimization
(including space-filling designs), and post-processing techniques such as probability
calibration.
• Build and deploy GenAI-enabled information extraction workflows including OCR, named
entity recognition (NER), NLP, and custom prompting schemas to pull structured data from
PDFs, scanned contracts, and other unstructured documents across systems.
• Deploy trained model objects and workflows into production environments using Databricks,
APIs, SQL (for in-database inference using frameworks like Orbital), or containerized
services.
• Develop and deliver upskilling content, tutorials, and hands-on workshops for internal
RADAR team members and extended data scientists at Jeppesen ForeFlight covering git
and version control, scripting in R and Python, boosting productivity with GenAI tools, and
core data science concepts.
• Partner with cross-functional stakeholders to translate ambiguous business questions into
well-scoped analytical projects, and communicate findings to both technical and non-
technical audiences.
• Contribute to the team’s standards for reproducible, version-controlled analytical work.
• Design, build, and maintain reproducible end-to-end machine learning pipelines for
multivariate regression and classification tasks using frameworks such as R’s tidymodels
and/or Python’s scikit-learn.
• Apply gradient boosting methods (XGBoost, LightGBM) and ensemble approaches (random
forests), among other ML and deep learning algorithms, to high-impact business problems.
• Implement rigorous data pre-processing, feature engineering, hyperparameter optimization
(including space-filling designs), and post-processing techniques such as probability
calibration.
• Build and deploy GenAI-enabled information extraction workflows including OCR, named
entity recognition (NER), NLP, and custom prompting schemas to pull structured data from
PDFs, scanned contracts, and other unstructured documents across systems.
• Deploy trained model objects and workflows into production environments using Databricks,
APIs, SQL (for in-database inference using frameworks like Orbital), or containerized
services.
• Develop and deliver upskilling content, tutorials, and hands-on workshops for internal
RADAR team members and extended data scientists at Jeppesen ForeFlight covering git
and version control, scripting in R and Python, boosting productivity with GenAI tools, and
core data science concepts.
• Partner with cross-functional stakeholders to translate ambiguous business questions into
well-scoped analytical projects, and communicate findings to both technical and non-
technical audiences.
• Contribute to the team’s standards for reproducible, version-controlled analytical work.
Basic Qualifications
• 5-10 years of applied experience in data science, machine learning, and/or quantitative
analytics.
• Strong proficiency in R and Python for statistical modeling, ML, and API pipeline
development.
• 5-10 years of applied experience in data science, machine learning, and/or quantitative
analytics.
• Strong proficiency in R and Python for statistical modeling, ML, and API pipeline
development.
• Hands-on experience building supervised learning models (regression, classification) using
frameworks such as scikit-learn, tidymodels, XGBoost, Stan, or similar.
• Demonstrated understanding of the full modeling lifecycle: data pre-processing, feature
engineering, hyperparameter tuning, model evaluation, calibration, and deployment.
• Experience with SQL and working against large-scale data warehouses or analytical
databases.
• Familiarity with NLP, text extraction, or document processing techniques (OCR, NER, or
similar).
• Excellent written and verbal communication skills, with the ability to present complex
analytical work to non-technical stakeholders.
• Bachelor’s degree in Mathematica, Statistics, Computer Science, Economics, or a related
quantitative field. Master’s degree preferred.
frameworks such as scikit-learn, tidymodels, XGBoost, Stan, or similar.
• Demonstrated understanding of the full modeling lifecycle: data pre-processing, feature
engineering, hyperparameter tuning, model evaluation, calibration, and deployment.
• Experience with SQL and working against large-scale data warehouses or analytical
databases.
• Familiarity with NLP, text extraction, or document processing techniques (OCR, NER, or
similar).
• Excellent written and verbal communication skills, with the ability to present complex
analytical work to non-technical stakeholders.
• Bachelor’s degree in Mathematica, Statistics, Computer Science, Economics, or a related
quantitative field. Master’s degree preferred.
Preferred Qualifications
• ML Frameworks & Workflow Design: Experience designing deterministic, reproducible ML
workflows using tidymodels (R) and/or scikit-learn (Python) pipelines, including space-filling
experimental designs for hyperparameter optimization.
• Modern Data Tools: Experience with Apache Arrow, DuckDB, or Polars for high-
performance in-memory data processing and ETL.
• Cloud & Compute Platforms: Experience with Databricks (mlflow, notebooks, Unity
Catalog, Spark) or similar cloud-based ML platforms.
• R Ecosystem: Proficiency with tidyverse, DBI, odbc, dbplyr, Shiny, and ellmer.
• GenAI & Agentic Tools: Experience using AI-assisted development tools such as Claude
Code, Codex, Cline, etc., for accelerating analytical and engineering workflows.
• CI/CD & Version Control: Experience with git-based CI/CD pipelines (GitLab CI/CD or
GitHub Actions) for automated testing, model validation, Quarto renderings, deployment
workflows, etc..
• IDE Proficiency: Comfortable working in VS Code, RStudio, or Positron (JetBrains DataGrip
a plus).
• Teaching & Mentorship: Track record of developing training materials, leading workshops,
or mentoring junior analysts and data professionals.
• Applied Statistics: Graduate-level coursework or professional experience in Bayesian
methods, mixed effects models, survival analysis, experimental design, time series analysis,
and/or pre-training transformers for event sequence problems.
• Cross-Industry Versatility: Experience working across multiple business domains (finance,
healthcare, operations, etc.) and adapting analytical approaches to varied problem types.
• ML Frameworks & Workflow Design: Experience designing deterministic, reproducible ML
workflows using tidymodels (R) and/or scikit-learn (Python) pipelines, including space-filling
experimental designs for hyperparameter optimization.
• Modern Data Tools: Experience with Apache Arrow, DuckDB, or Polars for high-
performance in-memory data processing and ETL.
• Cloud & Compute Platforms: Experience with Databricks (mlflow, notebooks, Unity
Catalog, Spark) or similar cloud-based ML platforms.
• R Ecosystem: Proficiency with tidyverse, DBI, odbc, dbplyr, Shiny, and ellmer.
• GenAI & Agentic Tools: Experience using AI-assisted development tools such as Claude
Code, Codex, Cline, etc., for accelerating analytical and engineering workflows.
• CI/CD & Version Control: Experience with git-based CI/CD pipelines (GitLab CI/CD or
GitHub Actions) for automated testing, model validation, Quarto renderings, deployment
workflows, etc..
• IDE Proficiency: Comfortable working in VS Code, RStudio, or Positron (JetBrains DataGrip
a plus).
• Teaching & Mentorship: Track record of developing training materials, leading workshops,
or mentoring junior analysts and data professionals.
• Applied Statistics: Graduate-level coursework or professional experience in Bayesian
methods, mixed effects models, survival analysis, experimental design, time series analysis,
and/or pre-training transformers for event sequence problems.
• Cross-Industry Versatility: Experience working across multiple business domains (finance,
healthcare, operations, etc.) and adapting analytical approaches to varied problem types.
Summary Pay Range
About Jeppesen ForeFlight
Jeppesen ForeFlight is a leading provider of innovative aviation software solutions, serving the
Commercial, Business, Military, and General Aviation sectors globally. Combining Jeppesen’s 90-
year legacy of accurate aeronautical data with ForeFlight’s expertise in cutting-edge aviation
technology, the company delivers an integrated suite of tools designed to enhance safety, improve
operational efficiency, and sharpen decision-making.
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