Alexander Peterson

About

Engineering Data Scientist at Boeing with expertise in machine learning, statistical analysis, and big data. I specialize in developing predictive models, time series forecasting, and communicating complex quantitative insights to stakeholders. Previously contributed to consumer analytics and financial health assessments at MiBosillo, working with billion-record datasets in PySpark.

Boeing | Engineering Data Scientist (2022 – Present)

I develop machine learning and generative AI solutions for engineering and business problems in aviation logistics, compliance, and safety. My work combines NLP, classification models, and RAG systems to improve operational efficiency and customer engagement.

Dangerous Goods Classification System

Built NLP classifier to predict whether goods in transit are dangerous, encoding part description data for probabilistic risk assessment
Implemented LLM solutions to summarize compliance documentation, reducing manual review burden for data stewards

Presented at: Boeing Technical Excellence Conference (BTEC) 2024, Advanced Visualization and Data Analytics Symposium (AVDATS) 2024

Parts Planning Hub AI Assistant

Developed retrieval-augmented generation (RAG) chatbot for airline customer support and onboarding
Enabled instant access to platform documentation, driving increased customer engagement and service data collection
Refactored data pipeline to rely on production sources, improving reliability and maintainability

Presented at: EDGE Conference 2026

Parts Data Optimization

Created Teradata production view that generated data for 3+ million parts in seconds—a process that previously took minutes per part in Microsoft Access
Eliminated bottleneck in parts data access, enabling real-time analytics

Current Work

Building web application to analyze laboratory test data for engineering teams
Collaborating with Safety Analytics to analyze fleet-wide telemetric data, understanding flight patterns to inform safer procedures and design choices

Presentations

Conference	Year	Title
EDGE	2026	Retrieval Augmented Generation for Customer Support and Onboarding within Parts Planning Hub
BTEC	2024	Predicting Dangerous Goods in Transit (DGIT) and Increasing Efficiency of DGIT Manual Reviews with Generative AI
AVDATS	2024	Predicting Dangerous Goods in Transit (DGIT) and Increasing Efficiency of DGIT Manual Reviews with Generative AI

Skills

ML/AI

Random Forests, ARIMA, Clustering, Feature Engineering, RAG Systems

Big Data

PySpark, Apache Spark, Teradata

Languages

Python, SQL

Tools

Kibana, Splunk, JIRA, Salesforce

Domain

Time Series Analysis, NLP, Statistical Testing, A/B Testing