Data Analyst Machine Learning

Damilola Dada

*********.****@*****.*** 337-***-****

PROFESSIONAL SUMMARY

Data Analyst with over 9 years of experience in transforming complex datasets into actionable insights and business solutions. Proficient in Python, R, SQL, and advanced statistical modeling techniques, with a strong background in data cleaning, analysis, visualization, algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing. Experienced in leveraging predictive modeling and machine learning to inform decision-making and optimize processes. Known for delivering clear, data-driven insights and collaborating cross-functionally to solve challenging problems. Passionate about turning raw data into strategic assets to drive growth and innovation.

TECHNICAL SKILLS

Programming: Python (NumPy, pandas, Scikit-learn, seaborn), R (ggplot2, dplyr, Caret), MATLAB, SQL, Pyspark, VBA, Databricks.

Data Engineering: ETL Processes, Data Cleaning, Feature Engineering, Database Pipeline management.

Data Analytics & Statistics: Deep Learning (ANN & CNN), PyTorch, Tensorflow, Ensemble Learning, Regression Analysis, Hypothesis Testing, NLP, Data Visualization (Matplotlib, Seaborn) Tableau, SPSS, SAS.

Tools: Jupyter Notebook, RStudio, Git/GitHub, Docker, Kubernetes, LLMs, Microsoft Office, Jira, Asana, Airflow, AWS, Ishbook, VScode, looker

Github: GitHub Link

EXPERIENCE

Data Analyst: Module Integration Engineer – Intel Corporation, Remote

May 2022 – Present

Spearhead machine learning operations for semiconductor manufacturing data, creating and maintaining GitHub repositories as in-house products for engineers.

Develop and optimize manufacturing processes using data-driven insights, leading to a 15% increase in production efficiency.

Implement predictive maintenance strategies by applying machine learning models to operational data, resulting in reduced downtime and improved equipment reliability.

Collaborate with cross-functional teams to design and deploy new manufacturing technologies, integrating data analytics into process development.

Conduct risk assessments and troubleshoot complex process issues using statistical analysis and simulation tools.

Present analytical findings and recommendations to senior engineers and management, contributing to strategic planning.

Research Data Analyst – Florida Agricultural & Mechanical University, Tallahassee, FL

August 2020 – April 2022, December 2022 – April 2023

Designed and conducted advanced data analysis on semiconducting oxides using Python and MATLAB.

Developed and optimized data processing python codes to improve accuracy and efficiency of computational models.

Extracted, transformed, and cleaned large datasets from multiple sources to ensure data accuracy and readiness for analysis.

Prepared data-driven presentations for national conferences and contributed findings to publications.

R & D Intern: Data Analytics – Lawrence Livermore National Laboratory, Remote

September 2022 – November 2022

Managed a cross-departmental project to establish a new data warehousing architecture, reducing data retrieval time by 25%.

Collaborated with research scientists to apply machine learning techniques in the identification and prediction of material defects.

Performed statistical analysis on experimental data, contributing to the development of new materials with enhanced properties.

Utilized Python, R, and SQL to preprocess data, addressing inconsistencies and resolving missing values to improve dataset quality.

Research Data Analyst – University of Louisiana, Lafayette

August 2018 – August 2023

Increased research efficiency by automating data-oriented processes by 15% using VBA.

Established new research intelligence reports using Tableau/R resulting in data-driven decision making.

Trained 20+ undergraduate students on data-cleaning techniques and data visualization tools.

Implemented new data collection techniques which reduced inaccuracy in data inputs by 30%.

Associate Data Scientist – Alegria Recyclers

November 2015 – July 2018

Updated company data warehousing techniques such as data recall and segmentation, resulting in a 20% increase in usability for non-technical staff members

Conduct data regression analyses of the relationship between company stock prices and industry trends, achieving a 15% more accurate prediction of performance than previous years

Deliver expert technical support to mixed signal customers, resolving complex issues and optimizing manufacturing processes.

Modernized data streamlining processes, resulting in a 47% redundancy reduction.

EDUCATIONAL BACKGROUND

Doctor of Philosophy: Physics (Materials and Data Science)

Florida Agricultural & Mechanical University Tallahassee, Florida, USA Dec 2023

Master of Science in Physics (Materials Data Engineering and Analytics)

University of Louisiana at Lafayette, Lafayette, LA Aug 2020

Bachelor of Science & Technology in Physics (Data Analytics)

Federal University of Technology, Akure, Ondo, Nigeria Oct 2015

Certifications

Google Data Analytics

IBM Professional Certificate in Data Science – Python, R, Data Cleaning

Data Science with Python Course – Hands-on Data Science (2021)

PUBLICATIONS

1. Resonant Ultrasound Applied to Additively Manufactured Alloys

2021 IEEE International Ultrasonics Symposium (IUS); doi:

Investigated the mechanical properties of additively manufactured alloys using resonant ultrasound techniques. Data analysis involved Python-based computational tools.

2. Quantum Plasmonics of Few Electrons in Strongly Confined Doped Semiconducting Oxide: A DFT+U Study of ZnGaO. Journal of Applied Physics; doi: 10.1063/5.0081075

Conducted a DFT+U study on semiconducting oxides, focusing on quantum plasmonics with data-driven modeling techniques.

3. First Principles Optical Study of Shallow Impurities in Lightly Doped ZnO Quantum Dots

Employed first principles and optical modeling to study shallow impurities in ZnO quantum dots, utilizing advanced data processing and computational techniques.

Buy and sell used cars

15 people

Work on building models different fraud risk mitigation models

2 open roles – scientist -finanace risk mitigation

-Informed output for estimate for customers

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