Machine Learning Engineer

Terragon Group

1. Informed business decisions through successful use of both existing and emerging data science principles.
2. Led the development and deployment of machine learning models that provided valuable insights into consumer behavior across diverse industries.
3. Spearheaded cross-functional collaboration, establishing scalable machine learning pipelines that significantly improved model accuracy and business outcomes.
4. Championed software engineering best practices through agile methodologies, ensuring code quality and version control compliance.
5. Identified trends in large datasets with application of analyses to fintech, FMCG research, and other key business areas.
6. Boosted design and deployment success by incorporating advanced machine learning techniques from latest research.
7. Built features for new products and updated functionality of existing software systems.
8. Optimized software usability and functionality by developing targeted algorithms.
9. Informed decision-makers about projects through documentation of specifications, activities and current status.

Dats Scientist

Insight7

1. Uncovered and developed critical insights using data mining and advanced algorithms in python.
2. Designed and deployed custom machine learning solutions to enhance product adoption.
3. Recommended data-driven solutions to help stake holders meet customers’ needs and resolve their problems.
4. Collaborated on A/B tests and experiments, driving improvements in data quality and product stability.
5. Translated complex insights into actionable business recommendations, utilizing impactful visualizations and presentations.
6. Developed innovative text processing features utilizing LLMs, enabling product managers to extract insights from reviews and research documents.

Data Analyst

Brainnest

1. Conducted comprehensive data analysis, uncovering customer behavior trends and informing sales strategy improvements
2. Summarized data analysis results in clear and compelling business report narrative.
3. Transformed raw data into actionable insights for internal teams.
4. Filtered and cleaned data with automated and manual data reviews.
5. Completed high throughput analytics using SQL.
6. Analyzed sales, circulation and usage data for business insights.
7. Developed reports and dashboards to report on key business intelligence.
8. Verified BI data, checking correctness, validity and structure.

World Quant University

Applied Data Science

ALX Africa

Data Analysis

Federal University of Technology Akure

Metallurgical and Materials Engineering