Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 4

1st Place,  Malawi Flood Data Visualization Challenge   |  (Jun 2020)
Nominee,  World Bank Geo Data4vernance Hackathon |  Feb 2020
3rd Place, Zindi Africa Uber Incidence Prediction Challenge  |  Jan 2020
Top 5 Best Data Scientist in DataScience Nigeria BootCamp  |  2018, 2019
Ranked top 1% Data Scientist of over 300,000 Data scientist in |  Current zindi.africa (Africa’s Biggest ML Competition Platform).
3rd Place,Xente Credit Scoring Challenge.                | Jan 2020
Pioneered the design and architecture of GIS map for  on-field data collection system. | Current
Architect an ETL process from diverse spatial data sources that guided navigation during data collection, resulting in 30% increase in enumeration time.	| Current
I am a Data Scientist with 4+ years of experience with proven track record in building data-driven products, machine learning and  geospatial-intelligent solutions across the health and retail industry.  I have built projects that cut across qualitative data enumeration, machine learning, computer vision, earth observation, data visualization, and applied research.

I have worked in:
-- Developing software from product specifications to deployment, given my strong background in computer engineering (Python, Javascript, HTML, CSS,Git, Elasticsearch, Docker, RestAPI, Django, Kubernetes, Spark )
-- Designing machine learning(NLP, Computer Vision) algorithms into production (NLTK, Scikit-Learn, Tensorflow, MLOPs, Distributed Training, Model Monitoring, MLflow).
-- Analysing & Visualizing data (,Tableau, Google Sheet, Panda, Rasterio, Matplotlib, Google Sheet, Google Earth Engine, QGIS)
-- Wrangling and Mining data from diverse sources  (PostgreSQL, ETL, BigQuery, Beautifulsoup, QGIS )
-- Community, Sharing, and Writing: Top % Data scientist in zindi.africa.
-- Documentation and blog writing

Some of my currently work include;
- I have led the collaboration and communication with a cross-functional team of software engineers, clients, and key stakeholders in eliciting product requirements into actionable business metrics.

- I have developed databases for datacollection to identify 12,000+ local drug store locations, revenue, exit rate, and accessibility opportunities using 1000+ crowdsourced community members.

- i have built an end-to-end deep learning model to classify Community pharmacies (CP) and Proprietary Patents and Medicine Vendors (PPMV) stores, achieved >90% accuracy using ResNet-50 with PyTorch resulting in 50% of data Q/A check-time.

- I have worked extensively in architecting ETL pipelines (Python, BigQuery) from spatial databases with Satellite imagery for interactive analytics dashboards and report building in location intelligence platform.
Vision Driven Team: Innovative with constant checks for growth at each iteration