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Data Science with 3+ years in Data Analysis & Python
AI is analyzing your overall score…
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Final-year Finance and Statistics student with a Diploma in Applied Statistics and practical experience spanning data analysis, relational databases, and Python scripting. Proven capability in utilizing Python and Pandas for data manipulation, cleaning, and structural problem-solving. Combines solid academic research foundations with hands-on software development to build privacy-conscious data models and custom business infrastructure. Seeking a position to deliver accurate solutions and analytical support.
Mount Kenya University
B.Sc. · Finance and Statistics
N/A – June 30, 2026
Kiambu National Polytechnic
Diploma · Applied Statistics
N/A – Present
PLP Academy
Certificate · Software Development (AI for SE)
N/A – June 30, 2025
Votex Ltd
Site Clerk
November 1, 2022 – November 1, 2025
Kiambu, Kiambu County, Kenya
Kiambu Level 5 Hospital
Industrial Attachment - Data & Records
September 1, 2019 – November 1, 2019
Kiambu, Kiambu County, Kenya
Company Operational Infrastructure Database & ETL Pipeline
June 24, 2026 – Present
Designed and mapped a physical Entity-Relationship (ER) diagram for an operational database consisting of 5 core relational entities (employees, departments, projects, dependents, and works_on). Implemented strict relational constraints, identifying primary keys (such as SSN and PNum) and complex foreign key relationships-including a composite primary key structure in the works_on junction table to track data mapping without duplication errors. Currently developing backend Python data pipelines using Pandas to ingest, clean, and validate simulated corporate records, ensuring strict datatype alignment with VARCHAR, INT, and DATE attributes. Mapping workflows to execute structural data analysis on project hours, with plans to export processed records into interactive Microsoft Excel spreadsheets and deploy a live data-validation interface via Streamlit.
Intelligent Hospital Management System (IHMS) - Full-Stack AI Application
June 24, 2026 – Present
Co-engineered an operational, full-stack digital solution combining a high-performance FastAPI asynchronous backend API and a Streamlit micro-frontend to address clinical attendance inefficiencies. Structured relational database models using SQLite (hospital.db) and SQLAlchemy ORM to manage data ingestion pipelines, safely transitioning clinical tracking records from manual filing methods to a stable digital ecosystem. Implemented an integrated machine learning algorithm (Scikit-Learn Random Forest Classifier) that evaluates client demographics and historical satisfaction variables to dynamically score individual appointment attendance probabilities. Integrated Plotly Express charts into the frontend layout to visualize real-time KPIs, monitor hospital survey data points, and generate risk-adjusted operational reminder recommendations for clinical staff. Configured production-ready environment architectures via a render.yaml specification to cleanly deploy and maintain a live-hosted interface and public endpoint services on the Render cloud platform.
Data Fundamentals
IBM
June 1, 2026 – Present
Big Data 101
IBM
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from operational infrastructure databases to intelligent hospital management systems, indicates adaptability and a broad interest in applying data science across different domains. Their academic background in Finance and Statistics, combined with a Software Development certificate, shows a multidisciplinary approach. The target role of Data Science aligns well with their project experience in machine learning, data pipelines, and statistical analysis. However, the professional experience outside of internships is not directly in a data science role, which might require some ramp-up.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical problem-solving skills, attention to detail in data management, and an understanding of workflow efficiency and data privacy protocols. Their experience in coordinating documentation flow and managing administrative data pipelines suggests good organizational and operational fit.