Am passionate about using data to develop models and drive product-focused solutions, particularly in data engineering and analytics. My expertise lies in tools like SQL, Python, DBT, PowerBI, Google Looker, and Excel, which I have utilized to create and optimize data pipelines, enhance customer insights, and deliver actionable reports that impact business decisions. I have a proven track record of building data-driven products in the fintech industry, including customer segmentation, funnel optimization, and enhancing user experiences through data analytics. For example, I implemented a sentiment analysis on customer surveys using PowerBI and Python, leading to product improvements adopted by the technology team to enhance user experience.
With strong skills in data modeling, data visualization, and critical thinking, I’ve successfully developed end-to-end data solutions that improve business performance. In my role as an Analytics Engineer, I’ve delivered impactful projects like the Flywheel Categorization Matrix, improving customer lifecycle management and increasing retention by 45%. I have also Optimized Customer Funnel from onboarding to loan repayment using SQL, Google Analytics, and Looker to improve customer experience, reducing loan default rates by 20% and delivered insights on customer segments that led to a 25% rise in repeat loan applications and 30% better retention, refining loan offerings and marketing strategies.
I pride myself on being a self-starter, problem solver, and excellent communicator with the ability to breakdown data to drive business driven decisions to stakeholder
I occasionally use different technologies & platforms, however my current favorites are: 
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I am passionate about the use of data, to come up with models to make data-driven decisions.
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Predictive Analytics and Machine Learning:
- Automobile Resale Value Forecaster
The "Automobile Resale Value Forecaster" is a machine learning project focused on predicting the resale value of automobiles using a regression model and a simple neural network. This project utilizes a simple neural network built with TensorFlow, providing insights into the resale value based on various vehicle attributes. Here I used the HorsePower of these Used Care to determine its resale value.
- Automobile Resale Value Forecaster
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Amazon Q Sentiment Analysis:
- Amazon Q Sentiment Analysis
This mini-project came as a result of my curiosity with the whole hype of AI which seems to be dominating the technology landscape. Amazon Comprehend and Amazon Sagemaker Studio played a crucial role in carrying out the Sentiment Analysis of Amazon Q. With Amazon Comprehend’s advanced NLP capabilities such as entity recognition, key phrase extraction, and sentiment analysis, I am able to uncover valuable insights from the tweets extracted from Twitter.
- Amazon Q Sentiment Analysis
👩🏽💻 All of my projects are available at Github
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