ERIC EZENWA

Aspiring Data Analyst with a postgraduate degree in Data Science and hands-on experience in statistical modeling, data visualization, and machine learning, seeking an internship at Visier. Eager to apply my strong analytical skills and proficiency in Python, SQL, and Power BI to model real-world business problems, identify key metrics, and deliver actionable insights. Committed to learning from industry leaders while contributing to impactful projects through independent research, stakeholder collaboration, and data-driven storytelling.

Analytical and solutions-driven Data Scientist with a postgraduate background in computational intelligence. Skilled in solving complex business problems through data modeling, statistical analysis, and machine learning. Experienced in reframing real-world challenges into data-driven strategies that enhance decision-making, improve efficiency, and deliver measurable impact. Strong communicator who bridges the gap between data insights and strategic business goals.

• ECDL Advanced Spreadsheets - BCS
• Power BI Essential Training - LinkedIn Learning
• Machine Learning Foundations - Coursera
• SEO I and SEO II - HubSpot Academy

Surrogate Modeling with Artificial Neural Networks (MSc Thesis), Resolved a performance bottleneck by developing ANN-based surrogate models that simulated expensive systems with 60% faster computation., Applied backpropagation and data modeling to create a generalizable surrogate solution., Used Python, MATLAB, and Alteryx to build, evaluate, and present findings. Music Mood Classification with Machine Learning, Classified audio mood from song features using supervised algorithms and feature selection techniques., Achieved over 80% model accuracy through feature selection, data cleansing, and performance testing., Tools: Python, Weka, MATLAB CRM System for Farm Equipment Supplier, Addressed client's CRM inefficiency by developing a full-stack solution integrated with SAP HANA and SQL Server., Conducted problem scoping, cost analysis, and requirement gathering in a team-based agile setting., Collaborated via GitHub to share updates and streamline delivery.

• Diagnosed inventory inefficiencies by building a predictive stock-level model, reducing waste by 15% for a gaming retailer.
• Reframed vague business issues into defined data problems, improving stakeholder buy-in and analysis accuracy at Hi-Tech Plus.
• Automated dashboard delivery processes to reduce manual reporting time by over 5 hours/week.
• Developed surrogate neural networks to replace computationally expensive simulations, improving model speed by 60%.

Python, SQL, R, PySpark, C, C++, Regression, Classification, Clustering, Neural Networks, Cross-Validation, Hypothesis Testing, A/B Testing, ANOVA, Metrics Definition, Power BI, Alteryx, Google Data Studio, Excel, Jupyter, Microsoft SQL Server, SAP HANA, Azure ML, Analytical Thinking, Independent Research, Communication, Problem Framing, Teamwork