About Me

I'm Titilayo Amuwo, a versatile professional with a background spanning Financial analysis, Data science and Software engineering. I embarked on a career transition journey from the accounting industry to data science to develop a comprehensive understanding of data analytics and machine learning. During this transition, I’ve been honing my skills as an innovator, developing and optimizing machine learning models to tackle complex challenges, and my experience as allowed me to derive actionable insights from data to drive informed decision making. I believe in the power of collaboration and continuous learning, that is why throughout my career, I’ve collaborated with cross-functional teams to deliver impactful solutions and drive organizational success. My commitment to excellence, and passion for innovation have led to notable achievements, including developing predictive analytics models that reduced forecasting errors by 15% and increased efficiency by 20%. In today’s fast-paced and interconnected world, I am excited to continue leveraging my unique skill set to contribute value to companies at the intersection of data, and technology. Whether it’s optimizing machine learning algorithm, harnessing the power of data to drive business insights, or implementing cutting edge technology solutions, I'm dedicated to making a meaningful impact.


Let’s connect and explore how we can work together to turn ideas into reality!

  • Technical Tools
  • Python
  • SQL
  • Data Analytics and Machine Learning Tools
  • Web Development Framework
  • Java
  • Skills
  • Team-work
  • Communication
  • Inference
  • Problem-solving
  • Analytical thinking
  • Data Mining
  • Machine learning
  • Artificial Intelligence
  • Financial Analysis and Reporting
  • Market Research and Analysis
  • Software Development
  • Statistical Analysis
  • Feb 2022 - Present
    Machine Learning Engineer at Omdena
  • Feb 2023 - Aug 2023
    Information Technology Intern at Rubric
  • Jun 2022 - Nov 2022
    Political Technical Specialist at Zinc Collective
  • Aug 2021 - Dec 2021
    Data science student at Flatiron School
  • Jul 2020 - Aug 2021
    Accounting Bookkeeper at Intuit
  • Jun 2020 - Apr 2021
    Staff Accountant at The Mercury Group
  • Feb 2018 - Jun 2020
    Accounting Clerk at Penske Automotive Group
  • 2021
    Bootcamp Technical Certificate in Data Science from Flatiron School
  • Flatiron

  • 2019
    BSc in Accounting from University of Bridgeport
  • 2015
    BSc in Business Administration and Management from University of Lagos
  • Oct. 2023
    Getting started with Networking, AWS
  • Oct. 2023
    Web Application Development Builder, AWS
  • Sept. 2023
    AWS Machine Learning Fundamentals, Udacity
  • Udacity

  • Oct. 2022
    AWS AI Programming with Python, Udacity
  • Udacity

  • Sept. 2022
    Diagramming Foundations, Lucid
  • Apr. 2022
    Certified Cloud Practitioner, AWS
  • Dec. 2021
    Master SQL for Data Science, Udemy
  • Udemy

Projects

Soccer Match Betting

In this project, I employed a methodology that integrates various machine learning algorithms to develop a soccer forecasting model. The model predicts match outcomes based on a power index, historical performance, and odds data.

Landmark Tagging for Social Media

In this project, I developed a Convolutional Neural Network (CNN) algorithm and an accompanying application capable of accepting landmark images and predicting their locations.

Scone Unlimited Image Classification

In this project, I implemented a deep learning approach using AWS SageMaker to classify images, with the objective of developing a scalable and reliable model capable of distinguishing bicycles from motorcycles.

City Bike-Sharing Systems

In this project, I applied a range of machine learning algorithms on AWS SageMaker to forecast the usage patterns of bike-sharing systems in Washington, DC.

Data Analysis using Tableau

In this project, I leveraged publicly available data to create visualizations in Tableau Public for analysis and generating insights.

Flower Classification

In this project, I implemented a transfer learning approach using the VGG16_bn pretrained model to train a dataset of flower categories. The resulting model was then utilized to develop a command-line application capable of predicting flower categories.

Women In Data Science Datathon

In this project, I have employed various machine learning algorithms to predict the site energy usage intensity of buildings based on their characteristics and location-specific weather data.

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Blogs

Tableau: Do you really need it? this tutorial will help you decide.

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A summary of Data science and AI in finTech with examples of how it is used.

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A short tutorial on timeseries forecasting with Facebook Prophet.

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Contact

titilayo@abbyamuwo.com

Download Resume