Machine Learning Data Scientist

Emmanuel Atindama

Email LinkedIn GitHub New York, NY

Data Scientist Statistical Scientist Machine Learning Engineer Results-driven data scientist with 7+ years of experience in statistical modeling, data analytics, and machine learning. Expertise in statistical inference, experimental design, predictive modeling, Bayesian statistics, and risk analysis. Strong programming background with Python, R, SAS, and experience in data visualization, reliability analysis, and cloud computing. Education:

Clarkson University – PhD, Applied Mathematics (GPA: 3.89/4.0) Research Focus: Deep Learning, Image Processing, Statistics & Probability, Linear Algebra Sep 2021 – Aug 2024 Potsdam, NY

Clarkson University – MS, Applied Mathematics (GPA: 3.89/4.0) Coursework: Machine Learning, Numerical Analysis, Probability, Statistics, Data Mining Jul 2019 – May 2021 Potsdam, NY

University of Ghana – BS, Statistics (GPA: 3.24/4.0) Aug 2013 – May 2017 Accra, Ghana

Technical Skills:

● Statistical Analysis & Modeling: Bayesian Statistics, Experimental Design, Survival Analysis, Regression, Reliability Analysis

● Programming & Software: Python (Pandas, NumPy, Scikit-learn), R, SAS, MATLAB, SQL, C++

● Data Analytics & Visualization: OpenCV, Plotly, Matplotlib, Tableau, ggplot2

● Machine Learning & AI: TensorFlow, Keras, PyTorch, Scikit-learn, NLP

● Database & Cloud Computing: MySQL, SQLite, AWS, Pandas

● Version Control & Web/Dashboard Development: Git, Flask, Streamlit, Dash Professional Experience:

Visiting Assistant Professor – Data Science

University of Toledo Aug 2024 – Present Toledo, OH

● Designed and taught Introductory Python for Data Scientists, covering statistical programming and data manipulation.

● Developed and led courses on Data Visualization (Python-Streamlit & R-Shiny).

● Instructed SAS for Data Scientists, focusing on data wrangling, macro development, and statistical inference.

Statistician (Intern)

Regeneron Pharmaceuticals May 2023 – Aug 2023 Troy, NY

● Designed statistical experiments for bioassay drug efficacy tests.

● Conducted statistical risk analysis to improve change control management.

● Developed a Python-Dash dashboard for real-time monitoring of statistical processes.

● Applied Design of Experiments (DOE) techniques to optimize lab tests using JMP. Data Scientist

Clarkson University Sep 2021 – May 2023 Potsdam, NY

● Analyzed student retention & graduation data using SQL and R, identifying key influencing factors.

● Developed predictive models to improve student success initiatives using statistical inference tools.

● Presented findings to university administration, impacting policy decisions. Robotics Research Fellow

Clarkson University Aug 2023 – Aug 2024 Potsdam, NY

● Instructed students in circuit building and C++ programming for Arduino kits.

● Mentored students in research methodologies and experimental design. Machine Learning Engineer

National Coffee Corp. Jun 2021 – May 2022 Remote

● Developed a predictive model for food production & staffing using weather and foot traffic data.

● Mined and analyzed historical weather & sales data, performing feature selection for model training.

● Trained random forests, gradient boosting, and logistic regression models in Python.

● Reduced overstaffing and food waste by 30% within 3 months. Selected Research Projects

● Crystallographic Data Restoration: Developed Python algorithms to enhance EBSD image reconstruction using Total Variation Flow and deep learning models.

● Noise Estimation in 3D Data: Built a volumetric noise estimation algorithm to improve material data accuracy.

● Real-Time Object Tracking & Drowsiness Detection: Built computer vision models for safety applications.

● Statistical Analysis in Education: Led retention studies using statistical inference & Bayesian modeling.

Certifications

● Python (Basic) Certification – HackerRank ID: 447853C2B7BE

● SQL (Basic) Certification – HackerRank ID: 5E588A452CF9 Publications & Presentations

● Crystallographic Data Restoration Using Weighted Total Variation Flow & Hybrid Deep Learning – SIAM IS24

● Advances in Parameter-Free Reconstruction of Grain Orientation Data – SIAM MS24

● Measuring the Impact of Student Success Retention Initiatives for Engineering Students

– RAPS Spring 2023

● Restoration of Noisy Orientation Maps from EBSD Imaging – Integrating Materials and Manufacturing Innovation Journal (Aug 2023)

● Impact of Targeted Interventions on Success of High-Risk Engineering Students – Frontiers in Education (2024, Approved)

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