Camille Girabawe
Verified Expert in Engineering
Statistics Developer
Camille is a data leader with a PhD in physics and a passion for machine learning and artificial intelligence. He has extensive experience building multi-tenant and multi-cloud solutions for B2B and B2C systems across various domains, such as marketing, finance, procurement, logistics, and operations research. He uses cutting-edge technologies in machine learning, deep learning, and GenAI to solve real-life challenges and deliver value to his clients.
Portfolio
Experience
Availability
Preferred Environment
Git, Linux, MacOS, Visual Studio Code (VS Code), Docker, SQL, OpenAI GPT-3 API, Azure, Google Cloud Platform (GCP), AWS CLI
The most amazing...
...thing I've built was vehicle dispatch solution with speech-to-text and trip prediction that alerts drivers and dispatchers.
Work Experience
Senior Machine Learning Manager
Adobe
- Led the design, implementation, and productization of real-time generative capabilities to empower a marketing chatbot that scales across multiple tenants in different industries.
- Developed AI-driven filters to help marketers extend their audience using historical and real-time data of campaigns' success and failure. Results are a lift of up to 25% on the audience and a boost of about 7% on the success rate.
- Led the development of a machine learning solution to optimize the right time to send marketing emails to increase the open rate. Current A/B testing results show a double open rate.
- Spearheaded AI projects for the conversation marketing platform, from text representation models to language models and natural language understanding, NLP, and computer vision.
Data Scientist
SAP Labs
- Developed real-time monitoring of procurement expenses to propose materials for renegotiated contracts. Procurement strategic purchasers can reduce the processing time from an average of two months to three days.
- Developed a machine learning model to assign a risk score to each purchase requisition to automatically approve it based on SAP WorkFlow data. Improved data consistency and reduced the approval time interval to seconds.
- Developed a machine learning solution for invoice-to-account matching to reduce processing time, improve consistency, and reduce related accounting errors and frauds.
PhD Research Assistant
Brandeis University
- Investigated synchronization in non-linear oscillators using the Belousov-Zhabontisky reaction as an experimental medium. My work involved designing experiments, data collection, analysis, and mathematical modeling.
- Built a computer vision and mechanically empowered robotic system to control all experiments autonomously. Reactions were generated in droplets and deposited on microlithographic chips.
- Developed a programmable illumination microscope controller to excite or inhibit droplets using different light colors. This was achieved using computer vision technology to track droplets and their status in real-time.
Experience
Programmable Illumination Microscope (PIM) Controller
https://www.youtube.com/watch?v=BsRsiweTfp0A combination of deterministic and machine learning models was implemented in Python to train a model that would learn the temporal oscillations of the chemical solution and determine which cells to inhibit/excite by exposing them to light.
This was part of my dissertation: https://search.proquest.com/openview/aa8113b66c0fcb2d9a4f97fe7cfc5091/1?pq-origsite=gscholar&cbl=18750&diss=y
Predicting Green Taxi Tips
https://github.com/kthouz/NYC_Green_TaxiData were obtained from the TLC Trip Record Data. After a deep analysis of features for statistical significance, two random forest models were optimized and combined to predict the tip with an MSE of about 14. Several features were revealed to be very significant such as whether a rider pays with cash or electronically, trip duration, and speed which would give an idea of traffic congestion.
https://camillegirabawe.shinyapps.io/nycgreentaxi/
Scoring Model for a Toptal Client
Tech Stack: Python, MongoDB, Node.js.
EDA Tool
https://www.youtube.com/watch?v=Q62jB0ZFv6M&t=1sPieEye
Skills
Languages
Python, SQL, R, JavaScript
Libraries/APIs
Pandas, SciPy, NumPy, Scikit-learn, REST APIs, TensorFlow, Keras, Selenium WebDriver, PyTorch, Jenkins Pipeline
Other
Machine Learning, Mathematical Modeling, Physics, Linear Algebra, Statistics, Artificial Intelligence (AI), Data Analytics, Chatbot, Data Engineering, Natural Language Processing (NLP), Data Scraping, Deep Learning, Software Development, Data Visualization, Web Crawlers, GPT, Generative Pre-trained Transformers (GPT), OpenAI GPT-3 API, OpenAI GPT-4 API, LangChain, Hugging Face, Generative Artificial Intelligence (GenAI), Natural Language Understanding (NLU), CI/CD Pipelines, Chatbots
Paradigms
Data Science, Automated Testing
Storage
MySQL, MongoDB
Frameworks
Flask
Tools
Git, Apache Airflow, MATLAB, BigQuery, AWS CLI
Platforms
MacOS, New Relic, Arduino, Google Cloud Platform (GCP), Linux, Unix, Visual Studio Code (VS Code), Docker, Azure
Education
Ph.D. in Physics
Brandeis University - Waltham, MA
Certifications
Computational Investing - Credential ID PPQHXX8CRWV7
Coursera
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