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Hello,
I am Ross.
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I take my work seriously and am committed to delivering high-quality results.
I am a self-motivated and detail-oriented individual with a strong work ethic and a passion for technology.
My credential-list is a testament to my dedication to continuous learning and professional development and I am proud to have recently completed my Master's degree in Data Science from Eastern University.
I am an accomplished Machine Learning Engineer with a strong technical background that spans multiple domains.
I have a proven track record of delivering high-quality solutions in the fields of data science, machine learning, and software engineering.
My expertise includes a deep understanding of machine learning algorithms, data analysis, and software development.
I pride myself on my ability to work collaboratively with cross-functional teams to solve complex problems and deliver innovative solutions.
Additionally, I come prepared to tackle development across the entire software development lifecycle, from requirements gathering to researching, synthetic data creation and model training to
deployment and maintenance.
I am passionate about leveraging technology to drive business success and am always eager to learn and grow.
Most recently, my experience includes working with large language models (LLMs) and generative AI as well as computer vision and it has allowed me the opportunity to hone my skills in
generative AI and deep learning which translates directly into actionable improvements for client products and services.
I am excited to continue my journey in this field and look forward to the challenges and opportunities that lie ahead.

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Python
- Numpy
- Pandas
- SKLearn
- Scipy
- Django
- BeautifulSoup4
- Selenium
-
Distributed Computing
- AWS
- Azure
- Kubernetes
- Docker
- Apache Spark
- Hadoop
- Linux
-
Machine Learning
- Tensorflow
- Pytorch
- Keras
- GPYopt/HyperOpt
- XGBoost
- Neural Networks
- Deep Learning
- Bayesian Methods
- AutoML
-
Statistical Programming
- GPy
- Tensorflow-probability
- Gaussian Processes
- Expectation-Maximization (EM)
- Generalized Linear Models (GLM)
- Markov Chain Monte Carlo (MCMC)
- Variational Autoencoders
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Natural Language Processing
- LangChain
- LangGraph
- LLMs
- NLTK
- SpaCy
- Transformer Models
- Hugging Face
-
Computer Vision
- Convolutional Neural Networks
- Fully Convolutional Neural Networks
- Open CV
- Localization
- Classification
- Face Recognition
- Generative Adversarial Networks
-
Reinforcement Learning
- Actor-Critic
- RLHF
- Proximal Policy Optimization
- State-Value iteration
- Policy iteration
- Q-learning
- Monte Carlo Tree Search
- SARSA
- Bandits
-
Data Visualization
- Tableau
- Power BI
- Matplotlib
- Seaborn
- Plotly
-
Math
- Linear Algebra
- Differential Equations
- Multivariate Calculus
- Analysis
- Inferential Statistics
- Descriptive Statistics
-
Economics
- Econometrics
- Game Theory
- Behavioral Economics
- Microeconomics
- Macroeconomics
-
Other Skills
- SQL
- HTML & CSS
- Excel
-
M.S. Data Science
- Eastern University
- Graduation Date: 08/2024
- 3.9 GPA
-
B.S. Mathematics and Economics
- The University of Pittsburgh
- Graduation Date: 12/2020
-
Advanced Data Science with IBM
-
- National Research University Higher School Of Economics
- Introduction to Deep Learning
- How to Win a Data Science Competition
- Bayesian Methods for Machine Learning
- Practical Reinforcement Learning
- Deep Learning in Computer Vision
- Natural Language Processing
- Addressing Large Hadron Collider Challenges by ML
-
SQL for Data Science
How do I stack up against the competition?
According to Workera.ai, a data science and machine learning industry standardized testing company,
-
Machine Learning:
88th percentile
-
Data Science:
96th percentile
-
Deep Learning:
88th percentile
-
Software Engineering:
87th percentile
-
Algorithmic Coding:
74th percentile
-
Mathematics:
85th percentile
In the domain of Machine Learning, I proudly earned a score above the typical mid-level data scientist at Amazon. For the Data Science component, I performed better than the median of all degree programs, including ivy league colleges. In the realm of Deep Learning, I exceeded the median performance of graduates from all Masters degree programs. In Algorithmic Coding, I scored 2 points below the median score of a Stanford PhD graduate in civil engineering. Mathematics is another strong suit of mine as I surpassed the median scores of graduates from all Masters degree programs.
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