Senior Data Scientist
2019 - PRESENTAdobe- Developed AI-driven filters to help marketers to extend their audience using historical and real-time data of campaigns' success and failure. Results are a lift of up 25% on the audience and a boost of about 7% on the success rate.
- Created a machine learning pipeline to score leads behavior at different stages of their marketing journey [project currently under development].
- Automated orchestration of multiple dockers and dataproc at different stages of the machine learning development cycle using Airflow DAGs.
Technologies: Machine Learning, Deep Learning, Statistics, Python, SQL, GCP, Airflow, Newrelic, InDesginData Scientist
2017 - 2019SAP Labs- Developed real-time monitoring of procurement spendings to propose materials for (re)negotiated contracts. Procurement strategic purchasers can be able to reduce the processing time from an average of 2 months to 1 day.
- Assigned a risk score to each purchase requisition in order to automatically approve it based on SAP WorkFlow data. Reduced the approval time-interval to seconds and improved consistency in approve.
- Reduced the processing time, improved the consistency and set up a new platform for fraud detection for AI-driven matching of an invoice to an account to be charged for an invoice without purchase orders.
Technologies: Machine Learning, Deep Learning, Statistics, Python, SQL, JavaScript, GCP, InDesign, TensorFlow
Camille Girabawe
Statistics Developer in Santa Clara, CA, United States
Member since July 27, 2019
Camille is a data-driven thinker and strategies with a Ph.D. in Physics from Brandeis University and professional experience in data science in the B2B and B2C world. He has data science, software development, and business skills to design, implement, and deploy on-premise or on-cloud AI solutions for business problems. Camille enjoys applying machine learning to different areas to solve real life challenges.
Camille is now available for hire
Experience
- Statistics, 9 years
- Python, 7 years
- Machine Learning, 7 years
- Software Development, 3 years
- Automated Testing, 3 years
- Data Science, 3 years
- SQL, 3 years
- Google Cloud Platform (GCP), 1 year
Availability
Full-time
Preferred Environment
MacOS, Linux, Atom, Git, HanaStudio
The most amazing...
...thing I've designed and implemented was an AI-driven app in S4HANA Procurement suite to propose materials for contract negotiation based on historical spending.
Employment
Experience
- Programmable Illumination Microscope (PIM) Controler (Development)https://www.youtube.com/watch?v=BsRsiweTfp0
Python-based app to control a multipoint focused microscope to run a light-sensitive experiment. Given a sample of light-sensitive and optically oscillatory solution compartmentalized on a 2D grid, the goal was to focus light on selected cells in order to excite or inhibit them such that the entire grid would be trained in unison (just like fireflies) or any other given structure.
A 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 (Development)https://github.com/kthouz/NYC_Green_Taxi
The goal of the project was to build a model that can predict the amount of tip a driver of a Green Taxi would receive at the end of his/her ride in NYC.
Data 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/
Skills
Languages
Python, SQL, RLibraries/APIs
Pandas, SciPy, Scikit-learn, TensorFlow, Keras, DaskOther
Machine Learning, Mathematical Modeling, Physics, Linear Algebra, Statistics, Deep Learning, Software Development, Data VisualizationParadigms
Data Science, Automated TestingStorage
MySQL, MongoDBFrameworks
FlaskTools
InDesign CC, BigQueryPlatforms
Google Cloud Platform (GCP), Linux, Unix
Education
- Ph.D. in Physics2011 - 2017Brandeis University - Waltham, MA
Certifications
- Computational Investing - Credential ID PPQHXX8CRWV7JULY 2016 - PRESENTCoursera
