Senior Data Scientist

Developing a multi-class classification model to predict plant composition and its spatial and temporal changes using predictors, including satellite image, climate time series, and other environmental data such as land cover, human footprint, bioclimatic, and soil variables. The goal is to enhance biodiversity management, improve species identification tools and support educational efforts.

Developing recommender systems using contextual bandits for an e-commerce platform.

Building Deep Neural network models that predict floods affected areas

Developing informed machine learning algorithms to enhance pedestrian detection, lane change and rule breaking scenarios in autonomous vehicles

Setup of big data pipelines in AWS for Test and Verification of ADAS functions

Created pipeline and dashboard for monitoring data ingest and storage using Elastic Stack (Beats, Logstash, Elasticsearch and Kibana).

Integrating new data by creating a full pipeline from ingestion to ETL process pipelines using airflow. The automated ETL pipelines extract 3 TB of data per month

Coordinating and collaborating with multiple departments and stakeholders to collect each team data requirements and developing a data strategy to support them in making data driven decisions

Developed machine learning algorithms used to increased efficiency in drug discovery by automating the identification of monoclonal wells

Build real time monitoring and advanced analytics of data from a battery storage farm using IoT Hub, Blob storage, Stream Analytics, SQL, Power BI

Working in an agile team, which strongly believes in the culture of shared code ownership, pair programming and code reviews

Developed quantum impurity model technique (NRG and Monte Carlo) to simulate strongly correlated electron systems in condensed matter physics

Created software in C/C++ for parallel computing using hybrid parallelization technique (MPI and OpenMP)

Developed software in python for data visualization using Seaborn