09/06/2022 updated
100 % available
Research Engineer (Machine Learning) | 6x Amazon Web Services Certified | Analytics Expert
Houston, TX, USA, USA
USA
BS - Finance, Masters of Science Finance, MBA StatisticsAmazon Web ServicesAmazon S3Data AnalysisArchitectureBig DataCloud ComputingInformation EngineeringApache HadoopPython (Programming Language)KinesiologyMachine LearningNumPyOpen Source TechnologyTensorFlowSciPy
- Background: The candidate is a 6x certified machine learning engineer (AWS Certified Machine Learning Specialty) with expertise in Python (Pandas, SciPy, Sklearn, NumPy, Keras), Deep Learning, SQL, Data Engineering, Cloud Solutions Architecture, Operations, Microservices, Docker, Kubernetes, IaaC, Big Data Engineering (AWS Certified Data Analytics Specialty – aka “AWS Big Data Specialty”).
- Expertise: Hands-on experience in the design, development, and deployment of deep learning architectures using the AWS and opensource tech stack including GAN, RNN, LSTM, CNN, XGBoost, Keras, TensorFlow, VGG-16, Factorization Machines, AWS Comprehend, Lex, LAMBDA, EMR, Hadoop, Kinesis, Seq2seq, S3, and SageMaker.
- Experience: More than 15+ years experience in analytics & machine learning (2x Masters: The University of Chicago, The University of Houston).
Languages
EnglishFluent
Project history
* Designed performed data analytics, and designed machine learning and deep learning models
using Python, Keras, TensorFlow, sklearn and xgboost to perform signal processing of
time-series events.
* Designed experiments, tested code, and built machine learning models that estimate the
probable peak demand for electric-load and extrapolating far beyond the limited historical
data.
* Analyzed the national grid and conducted detailed graph-theoretic analysis, using python,
Jupyter notebooks and networkx, to determine route-cause of alarm types. Stored, saved, and
pushed code to GitHub repository for version control and stored model artifacts using Joblib
for machine learning models.
* Designed machine learning models using RandomForestRegressor, StackingRegressor,
VotingRegressor to generate innovative modeling schema for time series models. Implemented,
configured, and tested machine learning and deep learning libraries and platforms (e.g.,
TensorFlow, Keras, XGBoost, LightGBM) for model testing and experimentation.
using Python, Keras, TensorFlow, sklearn and xgboost to perform signal processing of
time-series events.
* Designed experiments, tested code, and built machine learning models that estimate the
probable peak demand for electric-load and extrapolating far beyond the limited historical
data.
* Analyzed the national grid and conducted detailed graph-theoretic analysis, using python,
Jupyter notebooks and networkx, to determine route-cause of alarm types. Stored, saved, and
pushed code to GitHub repository for version control and stored model artifacts using Joblib
for machine learning models.
* Designed machine learning models using RandomForestRegressor, StackingRegressor,
VotingRegressor to generate innovative modeling schema for time series models. Implemented,
configured, and tested machine learning and deep learning libraries and platforms (e.g.,
TensorFlow, Keras, XGBoost, LightGBM) for model testing and experimentation.
* Worked collaboratively with a research team, pitched new ideas, interviewed executive
management teams. Managed consumer and technology portfolios which outperformed benchmark by
1000+ bps.
management teams. Managed consumer and technology portfolios which outperformed benchmark by
1000+ bps.
* Business process testing of IT controls & collaborating with functional teams while finishing
a masters.
a masters.