Data with less than a year in Data Analytics & Machine Learning
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Result-driven early career Data Analyst with hands-on experience delivering end-to-end analytical solutions for fintechs, technology, sports betting and gaming industry. Recently built and deployed a full suite of real-time executive, finance, product, operations and community dashboards in Metabase for a live sportsbook platform, writing complex SQL across 6 PostgresSQL Databases. Skilled in SQL, PowerBI and Python skills with experience translating business questions into actionable insights through KPI reporting, retention analysis and data quality investigations.
Les Cours Sonou University
B.Sc. Economics · Economics
N/A – June 30, 2019
247 Sport - Sports Betting and Gaming Platform
Data Analyst
April 1, 2026 – June 1, 2026
Nigeria
Freelance
Data Analyst Tutor
January 1, 2026 – March 1, 2026
India
Primo Academy (A subsidiary of Bluechip Technologies)
Data Analyst Intern
May 1, 2025 – August 1, 2025
India
Customer Lifetime Value (CLV) Analysis & Prediction
June 1, 2026 – Present
Built an end-to-end CLV analysis system on real e-commerce transaction data (UCI Online Retail Dataset). Applied BG/NBD and Gamma-Gamma probabilistic models for predictive CLV forecasting using the Lifetimes library. Engineered RFM features and applied K-Means clustering with PCA visualization to segment customers by long-term value. Delivered business insights and strategic recommendations for customer retention and revenue growth.
View ProjectCustomer Churn Prediction & Analysis
June 1, 2026 – Present
Analyzed customer transaction and interaction data to identify churn drivers. Built classification models (Logistic Regression, Random Forest) to predict churn. Used SQL and Python to extract, clean, and validate datasets. Created dashboards showing churn trends, risk segments, and KPIs. Documented metrics and insights for non-technical stakeholders.
View ProjectAuto Data Cleaning Agent (LLM + Multi-Agent System)
June 1, 2026 – Present
Designed an AI-powered pipeline that automates dataset cleaning, validation, and code generation using OpenAI GPT-40-mini, Python, and Streamlit. Integrated multiple agents: Planner, Code Generator, Critic, Executor, and Validator. Deployed on Streamlit Cloud with real-time file upload, cleaning, and CSV download capability. Demonstrated intelligent reasoning, automated code synthesis, and validation of cleaned data.
View ProjectCustomer Segmentation Using Machine Learning
June 1, 2026 – Present
Applied clustering and classification techniques to segment customers. Engineered features such as recency, frequency, and monetary value (RFM). Evaluated model performance and interpreted results for business use. Presented findings in a simplified, business-friendly format.
View ProjectAgentic AI with Andrew NG
Deeplearning.AI
June 1, 2026 – Present
Multi AI Agent Systems with CrewAI
Deeplearning.AI
June 1, 2026 – Present
Evaluating AI Agents with Arize Phoenix
Deeplearning.AI
June 1, 2026 – Present
AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents
Udemy
June 1, 2026 – Present
Data Science, Data Engineering & Machine Learning
Primo Academy (Valendictorian)
January 1, 2024 – Present
Python for Data Science & Machine Learning Bootcamp
Udemy
January 1, 2024 – Present
Modern Data Analyst (SQL, Python, ChatGPT for Data Analysis)
Udemy
January 1, 2024 – Present
Data Analysis for Beginners
Udemy
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a strong interest in customer-centric data problems (CLV, churn, segmentation) and emerging AI technologies. The freelance and tutoring experience, combined with multiple certifications, highlights a self-driven and continuous learning mindset. This aligns well with a culture that values initiative, practical application, and staying current with industry trends. The focus on delivering business insights and strategic recommendations also indicates a results-oriented approach.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project work (e.g., identifying reporting gaps, automating data cleaning). Their experience as a tutor suggests good communication and simplification skills, which are valuable for stakeholder interaction. The project diversity indicates adaptability and a willingness to explore new technologies, aligning with an operational fit for dynamic data roles.