Help & Supportcareers@gravityer.com
Data Science with 1+ years in customer analytics, BI, and predictive modeling
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Analyst with hands-on experience in customer analytics, business intelligence, and predictive modeling. Skilled in SQL (PostgreSQL), Python (Pandas, NumPy, scikit-learn), and Power BI (DAX) for building dashboards, automating ETL pipelines, and delivering stakeholder-ready reports. Project experience includes churn prediction, conversion funnel analysis, customer segmentation, and model evaluation (ROC-AUC, precision-recall). AWS-certified in AI/ML fundamentals. Proven ability to translate large-scale datasets into actionable insights across retail, finance, and education domains.
Saint Paul College
Educator
August 1, 2020 – June 30, 2022
University of Lagos
B.Sc. Biochemistry (Honors) · Biochemistry
August 1, 2019 – June 30, 2025
Codveda Technology
Data Analyst Intern
March 1, 2026 – April 1, 2026
India
5 Star Company
Credit Risk & Recovery Specialist
May 1, 2023 – December 1, 2024
India
Retail Customer Churn Analysis
March 1, 2026 – June 1, 2026
Problem: Predict churn probability and segment at-risk customers from a retail base of 300,000+ records to support targeted retention. Cleaned and engineered features on 300,000+ customer records - engagement frequency, recency, and transaction consistency - to improve model input quality. Built a logistic regression classifier (scikit-learn); achieved 82% accuracy, 79% precision, 75% recall - a ~28% improvement over baseline rule-based segmentation. Evaluated model performance using confusion matrix, ROC-AUC, and precision-recall analysis to determine the optimal decision threshold for business deployment. Segmented customers into low, medium, and high-risk churn tiers to create a prioritization framework for retention campaigns. Developed an interactive Power BI dashboard tracking churn risk distribution, Customer Lifetime Value (CLV), and retention KPIs for real-time stakeholder monitoring. Presented data-driven recommendations to prioritize high-risk segments and optimize engagement touchpoints, delivering a decision-support framework to the retention team.
View ProjectE-Commerce Marketing Analytics
February 1, 2026 – June 1, 2026
Problem: Diagnose conversion bottlenecks and identify highest-ROI channels across the marketing funnel using 120,000+ transaction records. Analyzed 120,000+ transaction records to map the full marketing funnel, identifying a 6.8% overall conversion rate and pinpointing underperforming acquisition stages. Segmented performance by marketing channel to quantify contribution to conversions and customer acquisition cost (CAC), supporting data-driven budget reallocation. Quantified the impact of discount strategies, identifying them as the primary driver of conversion behavior and sizing the revenue opportunity of pricing optimization. Built Power BI dashboards tracking campaign performance, CAC trends, and conversion rates; dashboards used to monitor performance across subsequent campaigns. Presented recommendations to reallocate spend toward top-performing channels, supporting a strategic shift in marketing budget allocation.
View ProjectRetail Sales & Customer Behavior Analysis (SQL)
January 1, 2026 – June 1, 2026
Exploratory SQL analysis on the Olist Brazilian E-Commerce dataset (100K+ orders) to uncover revenue trends, customer behavior, and seller performance. Wrote 10 analytical queries spanning monthly revenue trends, payment method breakdown, order cancellation rates, and average delivery time by seller state. Applied window functions (RANK, DENSE_RANK, NTILE, LAG, SUM OVER) to rank sellers by revenue, calculate month-over-month growth, and identify top 10% customers by lifetime spend. Built a multi-CTE RFM segmentation model (Recency, Frequency, Monetary) classifying customers into tiers — Champions, Loyal, At Risk, and Lost — to support targeted retention strategy. Structured all queries across modular .sql files with a documented README, covering business question, approach, and key findings for each analysis.
View ProjectData Analytics Virtual Experience Program
Deloitte Australia
June 1, 2026 – Present
Data Visualization: Empowering Business with Effective Insights
Tata Group
June 1, 2026 – Present
AWS AI Practitioner - Artificial Intelligence, Machine Learning, Generative AI, Responsible AI, Prompt Engineering, Data Governance & Security
AWS
March 1, 2026 – May 1, 2026
Cultural Fit Analysis
The candidate's project diversity, spanning e-commerce marketing analytics, retail sales analysis, and customer churn prediction, indicates a broad interest in applying data science across different business domains. The academic nature of most projects, combined with an internship and a non-technical full-time role, suggests a strong drive to transition into a data science career. The AWS AI Practitioner certification and participation in virtual experience programs (Deloitte, Tata Group) highlight a proactive learning attitude and a commitment to continuous skill development, which aligns well with a growth-oriented culture. The target role of Data Science aligns with the candidate's demonstrated technical skills and project focus.
Soft Skills & Operational Fit
The candidate demonstrates a structured approach to problem-solving, as evidenced by their project descriptions detailing problem statements, methodologies, and outcomes. Their experience in stakeholder reporting and dashboard development suggests good communication and presentation skills for conveying insights. The documentation of projects on GitHub indicates an understanding of collaborative development practices. The Credit Risk & Recovery Specialist role, while not directly technical, shows experience in managing large datasets, prioritizing tasks, and achieving targets, which are valuable operational skills.