About
Hello, I am a AI Engineer passionate about converting complex data into actionable insights and solutions, with a background in data analysis, statistical modeling, and data visualization.
I have versatile experience across healthcare, finance, retail, and technology industries, proficient in Python, SQL, Power BI, and Excel for data manipulation and analysis. Skilled in presenting findings clearly and compellingly to drive business growth, Committed to continuous learning and seeking new challenges to enhance skills and deliver value to clients and can turn complex problems into meaningful insights.
I am eager to contribute, grow, and provide significant value as part of a dynamic team.
Experience
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2024 — Present AI Engineer with a passion for simplifying complex data into actionable insights across healthcare, finance, retail, and technology sectors. Proficient in Python, SQL, Power BI, and Excel, with a knack for presenting intricate findings in an accessible way. Committed to continuous learning and delivering substantial value, eager to contribute to and grow with dynamic teams.
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SQL
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Python
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Power BI
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Excel
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Projects
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Nike Data Analysis Tool
Developed a comprehensive analysis tool for a college project focused on Nike, tailored to meet the specific needs of key business roles including CFOs and Supply Chain Managers. Utilized advanced data analysis techniques to ensure accurate, actionable insights across multiple business functions.
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Heart Disease Analysis Project
Data analytics project on a real-world heart disease dataset to identify key predictors of heart conditions. Conducted thorough Exploratory Data Analysis (EDA) using Python to visualize relationships between demographic/health indicators and heart disease likelihood. Developed a predictive model using logistic regression, selecting variables based on their significance and predictive ability concerning heart conditions.
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World Bank Operation Analysis Project
Leveraged analytical skills in Python and Excel to perform comprehensive data analysis, synthesizing complex information into actionable insights. and implemented a robust methodology for data integration, linking three key flat files to facilitate a comprehensive analysis of project needs and financial risks.
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E-Commerce Churn Prediction Project
In this project the focus was churn prediction and identifying customers that were most likely to like a service. This is important for most companies as acquiring new customers is costlier than retaining old ones. So, the results of Churn prediction help companies focus on customers that are likely to churn and therefore develop strategies for retaining those customers. Here I worked with E Commerce Customer data from Kaggle: I started by loading the necessary libraries, then loading the data file and then continued exploring and cleaning the data.
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AirBnB Location Analysis
In a project for a real estate company eyeing the Portland, Oregon market for short-term rental opportunities, I conducted a location analysis, incorporating a simulated ETL process to merge varied data sources. My methodology, refined through peer and instructor feedback, involved data cleansing and manipulations to prepare a comprehensive report. This report highlighted the ETL process and initial findings with visual data, providing strategic insights into potential revenue growth in the Portland real estate market for interested investors.
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Walmart Pharmacy Project
In this comprehensive college project, I conducted a detailed analysis of the pharmacy department's business viability for a general merchandse retailer in Portugal, akin to Walmart in the U.S., integrating a deep understandng of stakeholder needs with the shifting business model from superstores to more efficient formats. Through initial dialogues to clarify project requirements, I developed a proposal to address key questions, integrating various data sources and identifying additional needs for a thorough analysis. Successfully consolidating these sources into a single dataset using Excel and Power BI, I prepared an initial report for stakeholders. This report covered stakeholder requirements, data integration, necessary data cleaning, and initial findings, each supported by visual aids for clarity.