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Click here or scroll down to respond to this candidateCandidate's Name
Principal Machine Learning Engineer | Transforming Data Into
Intelligence | Expert In AI, ML, And Data Science | Driving Innovation
And Scalable Solutions
With 8 years of expertise spanning supervised and unsupervised learning, reinforcement learning, and
natural language processing, I excel in crafting cutting-edge solutions. Proficient in deep learning
architectures like CNNs, RNNs, and LSTMs, I leverage frameworks such as TensorFlow, PyTorch, and Keras
to drive innovation. My toolkit includes Gradient Boosting, Decision Trees, SVMs, KNN, and clustering
algorithms like K-Means. Skilled in Python, R, SQL, and Java, I'm adept at data manipulation,
visualization, and feature engineering. Experienced in model evaluation, hyperparameter tuning, and
regularization techniques, I ensure robust performance. Additionally, I bring expertise in cloud
computing, containerization with Docker and Kubernetes, and proficiency in tools like NLTK, SpaCy,
Gensim, and OpenCV.
Contact Work History
Address 2022-06 - Senior Machine Learning Engineer &
Lakewood, New Jersey , Current Data Scientist
07095 United States
Illumina Technology Solutons
Phone
PHONE NUMBER AVAILABLE Spearheaded the implementation of
advanced data analytics techniques,
E-mail
including machine learning algorithms such as
EMAIL AVAILABLE
random forests and gradient boosting, to
optimize university admissions processes and
Skills improve student retention rates.
Developed predictive models to forecast
student academic performance and identify
Unsupervised Learning
at-risk students, utilizing techniques such as
Reinforcement Learning
logistic regression and decision trees to
Natural Language provide early intervention strategies and
Processing (NLP) support services.
Utilized natural language processing (NLP)
Deep Learning
algorithms to analyze academic research
Neural Networks
publications and extract insights, aiding in
Convolutional Neural faculty recruitment and research
Networks (CNN) collaboration initiatives within universities
Recurrent Neural Networks Led initiatives to establish data-driven
(RNN) decision-making frameworks within universities,
leveraging data visualization tools such as
Long Short-Term Memory
Tableau and Power BI to communicate insights
(LSTM)
Gradient Boosting and drive strategic initiatives.
Collaborated with university stakeholders to
Decision Trees
develop personalized learning pathways for
Random Forest
students, integrating machine learning
Support Vector Machines algorithms to tailor educational experiences
(SVM) based on individual learning styles and
preferences.
K Nearest Neighbors (KNN)
K-Means
2019-04 - Mid-Level: AI Engineering Manager
2022-03
Transcure
Python
Led the integration of machine learning
R
algorithms into engineering design processes,
SQL utilizing techniques such as supervised
learning and neural networks to optimize
Java
product design and performance.
Scala
Developed anomaly detection systems for
MATLAB monitoring equipment health and detecting
TensorFlow potential failures in engineering systems,
employing techniques such as time series
Keras
analysis and clustering algorithms.
PyTorch Implemented predictive maintenance
Scikit-learn solutions using machine learning models to
forecast equipment maintenance needs and
XGBoost
prevent costly downtime in engineering
LightGBM operations.
CatBoost Utilized natural language processing (NLP)
techniques to analyze engineering
NLTK
documentation and extract valuable insights,
SpaCy aiding in the identification of design patterns
Gensim and optimization opportunities.
Led initiatives to establish data-driven
OpenCV
decision-making frameworks within
Pandas engineering firms, leveraging data
NumPy visualization tools such as matplotlib and
seaborn to communicate insights and drive
Data Cleaning
strategic initiatives.
Data Visualization
2016-06 - Machine Learning Development
Data Wrangling 2019-03 Associate
Feature Engineering Mantaq Systems
Feature Selection Implemented machine learning algorithms for
predictive modeling in construction projects,
Model Evaluation
leveraging regression analysis and decision
Model Selection trees to forecast project timelines and costs.
Model Validation Developed computer vision systems to analyze
construction site imagery and monitor
Cross-Validation
progress, utilizing convolutional neural
Hyperparameter Tuning
networks (CNNs) to detect safety hazards and
Regularization Techniques: assess work completion.
L1 & L2, Dropout Utilized natural language processing (NLP)
techniques to analyze construction
A/B Testing
documentation and extract key insights,
Statistical Analysis
aiding in contract management and risk
Graph Analytics assessment.
Network Analysis Engineered anomaly detection systems to
identify deviations from construction plans and
Web Scraping
specifications, employing techniques such as
Cloud Computing (e.g., clustering and outlier detection to flag
AWS, Azure, Google Cloud) potential issues.
Docker Collaborated with engineering teams to
integrate sensor data from construction
Kubernetes
equipment into predictive maintenance
models, reducing downtime and optimizing
equipment performance.
Education
Bachelor of Science: Computer
Sciences
Jersey State University
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