Data Scientist Machine Learning

Uohna Thiessen

Summary

With over five years of professional experience, as well as ten years in education, I am a highly skilled data scientist with a strong background in artificial intelligence and machine learning. My expertise lies in providing valuable insights through data-driven techniques and implementing AI/ML methods across different industries.

I specialize in predictive modeling and have successfully implemented advanced analytics solutions that have improved patient outcomes and enhanced customer satisfaction. My proficiency in AI/ML enables me to confidently tackle complex business challenges, to eventually increase value, and drive substantial revenue.

Experience

Artificial Intelligence Consultant/Data Scientist

DataScienceDoctor, LLC

Feb 2014 - Present

Led multiple ML/AI projects focused on business analytics, risk prediction, customer segmentation, using statistics, machine-learning and deep-learning

•Integrated complex ML algorithms into cloud platforms to monitor and optimize performance, maximize efficiency, and increase revenue

•Developed and maintained critical dashboards and reports to track business outcomes and inform data-driven decisions

•Collaborated with cross-functional teams to identify key business metrics and develop impactful visualizations that provided valuable insights and trends

•Mastered Python, Sklearn, Numpy, PyTorch, Tensorflow, Keras, BigML, AzureML, etc.

•Expertise in Hypothesis tests, Data preprocessing, EDA, Dimensionality reduction, Modeling selection- Classification, Regression, Clustering, Reinforcement learning, Deep learning algorithms

Senior Data Scientist

Meta Facebook

Jun 2022 - Mar 2023

Utilized ML & DL algorithms tools (proprietary software) to validate pipeline from production using SQL, Python & PySpark

•Developed visualizations and dashboards using Tableau, Power BI, & Google Sheets to enhance monitoring efficiency.

•Conducted audience analyses and developed models AI/ML using in-house applications for customer segmentation.

Data Scientist

McKinsey & Company

Jan 2021 - May 2022

Analyzed trends and patterns within datasets using ML methodologies.

•Collaborated with client teams to structure and interpret data-related requests.

•Researched and developed statistical models to analyze complex data sets and provide insights to stakeholders.

Business Intelligence Analyst

BCBS

Feb 2019 - Dec 2020

Analyzed trends in healthcare data and developed data models and dashboards.

•Conducted exploratory data analysis to identify patterns and insights and communicated findings to stakeholders.

Adjunct Lecturer

Caltech CTME

May 2020 - Feb 2023

•Taught statistics, ML, DL, NLP, and AI with a 90% student satisfaction rate.

•Supervised student projects and mentored students in their academic and career goals as an adjunct lecturer.

Data Science Lecturer

Flatiron School

Jun 2021 - Mar 2022

Developed and presented statistics & machine learning lecture content.

•Conducted assessments and provided feedback on student work.

•Collaborated with the curriculum team to develop and improve course materials.

Instructional Design Consultant

Practicum USA

Mar 2021 - Jul 2021

•Collaborate with staff to design effective learning materials for ML courses

•Use e-learning tools and platforms to create interactive modules and assessment activities

•Gathered and analyzed feedback data to optimize instructional materials

Adjunct Lecturer

Kettering College

Jan 2020 - Aug 2021

Develop and deliver course materials, lesson plans, and tailored syllabi.

•Engaged students through interactive lectures, discussions, and activities

•Created projects and analysis activities to assess students' understanding.

•Continuously improved course content and teaching methods based on industry trends and academic research.

Graduate Research & Teaching Assistant

Wright State University

Aug 2008 - Jan 2013 (4 years 6 months)

•Support faculty in course preparation and class management

•Engage with students through tutorials, labs, and study groups

•Evaluate student work and provide constructive feedback to aid academic progress

Education

Walden University

Ph.D, Epidemiology, Biostatistics Emphasis

2013 - 2018

Dissertation research focused on disease prediction modeling.

Oakwood University

B.Sc, Biochemistry (Plant Electrophysiology)

2001 - 2003

Wright State University

A.B.D., Biomedical Science, Neuroscience

2008 - 2013

Completed all course work and passed comprehensive exams.

Skills

artificial intelligence • business intelligence • content management • critical thinking • data analysis • lecturer • Machine Learning • Statistical Data Analysis • Deep Learning • Generative Neural Networks

PROJECTS

Generative AI Ed Tech System: I developed an AI-powered personalized learning platform that utilizes machine learning (ML) and natural language processing (NLP). It monitors students' progress, identifies their strengths and weaknesses, and generates customized content. It improves learning efficiency, reduces achievement gaps, and alleviates teachers' workload.

Optimized CHD Prediction ML Model: I optimized a machine learning model to predict the onset of coronary heart disease with a 92% recall rate. It helped healthcare professionals more effectively detect and prevent this serious health condition.

Designed SUD Readmission Risk Model: I created a machine learning model that was able to forecast the probability of SUD patients being readmitted within 30 days from their initial discharge. The model achieved an AUC score of 0.85, which indicated its high level of accuracy and reliability.

Formulated EHR Data Pipeline: I created a pipeline for processing EHR data of patients with liver disease. The pipeline improves diagnosis, treatment, and management, enabling healthcare professionals to extract relevant data from EHR quickly and accurately for better decision-making and superior care.

Developed Liver Disease AI/ML Model: By utilizing advanced data analytics and feature engineering techniques, I developed a new method that significantly enhances the accuracy of liver disease detection. This feature has been integrated into a virtual health assistant platform that can effectively identify patients who are at a higher risk of developing liver disease.

Developed Cancer Detection Model: I have successfully developed a powerful ML model that uses a Convolutional Neural Network (CNN) to accurately detect cancer cells in breast cancer imaging data. This cutting-edge technology has the potential to significantly improve the speed and accuracy of cancer diagnosis, which can help facilitate timely and effective treatment.

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