ML Engineer
Machine Learning Engineer
Available for hire
Years of
experience
3+ years
Experience
level
Mid-level
I am an ML Engineer and Quantitative Data Analyst with over 3 years of experience building end-to-end machine learning pipelines and deploying scalable AI solutions. I have a proven track record of optimizing high-volume datasets (15M+ records) and developing predictive models for logistics, financial digitization, and geospatial analytics. I am looking for a mid-level remote role where I can leverage my expertise in Python, SQL, and cloud deployment to drive strategic business intelligence. What sets me apart is my ability to bridge the gap between complex backend data architecture and clear, actionable insights for non-technical stakeholders.
Skills
Employment History
ML Engineer at
TechnoColabs Softwares
2024
-
2025
• Designed, developed, and deployed an end-to-end machine learning pipeline processing ~15M rows of pickup and delivery operations data across 5 regional subsets.
• Cleaned and prepared large-scale GPS/operational data; engineered features including Haversine distance, log transforms, and PCA dimensionality reduction.
• Benchmarked multiple ML models (Linear, Ridge, Random Forest, XGBoost); achieved R² ≈ 0.92 (pickup) and ≈ 0.95 (delivery).
• Containerized FastAPI prediction service using Docker, deployed to cloud infrastructure, and integrated Prometheus/Grafana for real-time latency and uptime monitoring.
Data Scientist Trainee at
GoMyCode
2023
-
2024
• Built sensor-based predictive maintenance models using Random Forest, achieving >90% failure prediction accuracy deployed on a Streamlit prototype.
• Developed interactive Tableau dashboards and SQL queries to visualize key operational metrics for non-technical stakeholders.
• Conducted exploratory data analysis (EDA) using Python and SQL in an agile sprint environment to deliver recurring business insight packs.
Financial Digitization & Business Intelligence Specialist at
Seven Stars Bakery
2022
-
2024
• Led the digitization of daily sales reconciliation by deploying a PostgreSQL database, cutting financial reporting time by 85% and eliminating manual accounting errors.
• Automated weekly KPI tracking and OpEx reporting, reducing owner reconciliation effort from 3–4 hours to under 30 minutes.
• Performed data cleaning and quality assurance across 3,000+ historical transaction entries to ensure data integrity before database ingestion.
• Analyzed shift-by-shift sales logs to uncover peak purchasing hours, directly reducing end-of-day product waste.
Education
MSc Data Science and Analytics at
American Univeristy of Science and Technology
2024
-
2026
BSc Petrocheical Engineering at
Kwame Nkurumah University of Science and Technology
2016
-
2021