CV/Mission Pandas freelance

Aperçu des emplois de Moukouba,
freelance PANDAS résidant dans la Gironde (33)

PROFESSIONAL EXPERIENCE

09/2022 – : CGI, Bordeaux, France, Cloud Data Engineer
 Engineered and monitored data pipelines utilizing Azure cloud.
 Developed Machine Learning solutions for the prediction of water resources demand in the Bordeaux metropolitan area.
 Developed Object Detection and Tracking systems based on YOLO architectures.
 Created, updated, and maintained Machine Learning pipelines, for various applications, using MLOps tools.
 Developed, deployed, and managed web applications along with back-end Data/Machine Learning APIs.
 Ensured the scalability and reliability of cloud-based data systems.

05/2021 – 07/2022: Expleo Group/Flying Whales, Cross Department Meteorology and Data Science Referent
 Conducted data extraction and assessed outputs from Numerical Weather Prediction models and carried out geospatial data
analysis and mapping on Copernicus datasets.
 Built a cloud based and on-premise terabyte-scale data warehouse.
 Developed and optimized a decision-support software tool (Python-based) to facilitate operations, mission planning,
scheduling, and real-time assistance, using parallelization techniques for CPU and GPU to improve computational efficiency.
 Served as an in-house expert, providing assistance across all departments in design activities, and mission planning and
definition for airship construction and operation.
 Undertook climatological studies, mined data, and developed machine learning models for climate/weather prediction in
targeted areas and anchor sites for airship operations.

11/2015 – 12/2018: ANAC/National Dept. of Meteorology, Congo Climate Data Scientist
 Developed operational tools in Python for statistical climate modeling and forecasting, with applications across sectors such as
agriculture, rural community activities, health, energy, and construction.
 Performed geospatial data analysis and mapping on various GIS platforms.
 Performed environmental and health risk analyses, employing Bayesian Probabilistic modeling.
 Extracted, preprocessed, and derived new local variables via feature engineering on heterogeneous data obtained from various
sources, including in-situ sensors, satellites, and reanalysis.
 Developed Machine Learning models for climate forecasting, and Computer Vision systems for earth observation.
 Maintained, fine-tuned, and scaled models; automated processes and managed the national climatic database.
 Regularly communicated findings, attended global conferences and workshops, shared knowledge through internal training
sessions, and mentored interns.

03/2015 – 10/2015: Dept. of Mathematics of the Autonomous University of Barcelona, Intern
 Received mentorship in statistical modeling and operational research methodologies.
 Conducted research on Machine Learning algorithms, focusing on Low-Rank Matrix Factorization for Recommender Systems,
large Matrix Completion, as well as data smoothing and filtering.
 Implemented, from the ground up, algorithms including Maximum Likelihood PCA, Singular Value Thresholding, and Soft-Impute Alternating Least Squares as integral components of Master's Thesis research.

10/2012 – 09/2014: ANAC/National Dept. of Meteorology, Congo, Meteorologist
 Developed operational tools in R for weather and seasonal climate forecasting as well as air pollution dispersion prediction.
 Conducted river flow modeling and flood forecasting; analyzed associated risks and impacts across various socioeconomic
sectors.
 Designed agricultural experiments and forecasted crop yields using climatic data.
 Coordinated a national project on air quality monitoring and development of biometeorological products.
 Led the task force on national climate watch, managed the national climatic database, and produced seasonal climate outlooks
for a variety of end-users such as the general public, agriculture sector, health care, energy providers, transportation, and the
construction industry.
 Ensured effective dissemination of information to both technical and non-technical end users, while enforcing the WMO
Quality Management System.
 Represented the country as needed, with various external entities, such as acting as Focal Point for the ICAO – EU Carbon
Offsetting and Reduction Scheme for International Aviation (CORSIA).

CODING CHALLENGES

2023: Overstory Case Study
Cloud segmentation using Sentinel-2 satellite images
 Method: Implementation of a Deep Unet-like model with Efficient Inception block for Semantic Segmentation;
 Tools: Python, Pandas, OpenCV, Sklearn, Rasterio, TensorFlow/Keras.

2022: VEOLIA Challenge
Predicting odor compound concentrations (Air SO2 concentration forecasting)
 Method: Hybrid CNN-GRU Model for Parallel Multiple Inputs Multivariate – Multi-step time series forecasting.
 Tools: Python, Pandas, TensorFlow, Keras.

2021: EDF R&D Challenge
Individual household electric power consumption forecasting
 Method: Implementation of a Transformer architecture for Multivariate – Multi-step time series forecasting.
 Tools: Python, Pandas, TensorFlow, Keras.

2021: Schlumberger Challenge
Sinusoid segmentation in subsurface images
 Method: Implementation of a Deep Unet-like model with nested Inception blocks for Semantic Segmentation;
 Tools: Python, Pandas, OpenCV, Sklearn, Skimage, TensorFlow, Keras.

2020: Kaggle Challenge
Global Land Average temperature time series forecasting (see code on github).
 Method: Implementation of a GRU architecture (Gated Recurrent Units), with hyperparameter optimization.
 Tools: Python, Pandas, Sklearn, Plotly, TensorFlow, Keras, Talos.