Dillon Camp

Data Scientist and Cybersecurity Engineer

About Me

Dillon Camp is a Data Scientist and Cybersecurity Engineer at T-Mobile. He began his career at T-Mobile by designing and implementing a company-wide customer scoring system to identify the optimal customers for new product offerings, such as wireless home internet.

Dillon then joined the Cyber Security team to work as ingestion lead for the Cyber Security Data Platform — a revolutionary “Big Data” security solution that ingests, normalizes, enriches, triages, and manages application and security data at scale in real-time.


Solving cybersecurity problems by implementing innovative machine-learning and data science techniques.



Cybersecurity Engineer

February 2019 - Present

– Collaborate to create and maintain Cybersecurity Data Platform – revolutionary platform for logging and responding to security events:

  • Reduce security event search time from weeks to minutes
  • Lead log ingestion and create data flows using NiFi and MiNiFi
  • Mentor coworkers and delegate ingestion tasks
  • Implement machine learning to cluster events and identify threats
  • Enrich existing data to make it better (feature engineering)
  • Create monitoring using Grafana, Prometheus, and Pager Duty
  • Create Slack chatbot to aid in monitoring

– Create AI log parser that automatically parses security event logs using machine learning and natural language processing

– Develop single pane of glass search which cuts incident response time by 50% by enhancing all security events with username, server/computer, and Active Directory domain information

– Enable 2500 IDS/IPS signatures by creating a vulnerability scan dashboard in Splunk to intelligently determine which signatures to deploy

Offering consumers a better home internet solution.



Associate RAN Engineer

June 2018 - February 2019

Create and automate company-wide customer scoring system using Alteryx and Python to rank over 40 million customers for new product offerings:

  • Clean and combine data from multiple sources
  • Use machine learning to determine customer locations
  • Coordinate with other departments to determine most profitable target customers

NPPI All Star of the Month – November 2018


Machine Learning

XGBoost, Random Forest, Neural Networks, Naïve Bayes, Logistic Regression, Support Vector Machines (SVM), K-means clustering, Principal Component Analysis (PCA), Natural Language Processing (NLP), etc.

Big Data

Splunk, Spark, Kafka, Google Compute, Azure, Storm, NiFi, MiNiFi, Metron, Kibana, Elasticsearch

Programming Languages

Python, R, Java, SQL, Matlab/Octave, some Javascript

Data Analysis/Visualization

Tableau, Alteryx, Matplotlib, Pandas, Seaborn, ggplot2, MapInfo


Grafana, Prometheus DB, PagerDuty


U.S. Patent Application 16541121

Filed August 2019

Camp, Jat, Dousson, Sandhu, Escudero 2019. Customer Experience Scoring on Mobile Network Systems and Methods. U.S. Patent Application 16541121, filed August 2019. Patent Pending.

U.S. Patent Application 16529697

Filed August 2019

Camp, Jat, Dousson, Sandhu, Escudero 2019. Dominant Customer Locations Identification System and Methods. U.S. Patent Application 16529697, filed August 2019. Patent Pending.


Machine Learning by Stanford • MySQL by Duke University • Python and R by DataCamp • Six Sigma White Belt • Six Sigma Yellow Belt in progress


Detecting Credit Card Fraud with Cloud Computing

I use Python on a Google Compute virtual machine to analyze anonymized credit card transactions and successfully detect fraud with an F1 score of 85.68 and an accuracy of 99.96% (compared to a baseline accuracy of 99.83% when predicting that no transactions are fraudulent).

Indeed.com NLP Text Classification Using R


Using natural language processing (NLP) in R to classify Indeed.com job descriptions as Data Scientist or Data Analyst.

Spam Classification Using a Support Vector Machine in Python and R


Spam Classification Using a Support Vector Machine in Python and R

Using a support vector machine (SVM) in Python and R to classify emails as spam or not.

Image Compression Using K-means in Python and R


Using K-means clustering in Python and R to reduce image size.

Image Compression Using Principal Component Analysis (PCA) in Python and R


Using PCA to reduce the size of facial images in both Python and R.


Baylor University

Master's Degree, Economics


• Full Scholarship, Dean’s List

• GPA: 3.96 / 4.0

Baylor University

BBA, Finance


• National Merit Scholar – Full Scholarship, Dean’s List, Beta Gamma Sigma

• GPA: 3.98 / 4.0