| | | 20,000+ Fresh Resumes Monthly | |
|
|
| | Related Resumes Scrum Master Machine Learning Plano, TX Information Technology, Machine Learning Dallas, TX Software Development Machine Learning Frisco, TX Python Developer Machine Learning Irving, TX Big Data, Machine Learning, ETL and ELT and Data Analysis Denton, TX Machine Learning Power Bi Irving, TX Machine Learning Power Bi Dallas, TX |
|
Candidate's Name
Principal Machine Learning Engineer | Senior Data Scientist
Expert Data Scientist & Machine Learning Engineer with a comprehensive background in designing,
developing, and deploying machine learning models and big data solutions. Skilled in both data
architecture and Databricks,
Proficient in a range of programming and scripting languages including Python, Java, and Scala.
Experienced in leveraging libraries like TensorFlow, Keras, and PyTorch to engineer advanced AI
solutions.
Deep Learning Specialist with expertise in CNNs, RNNs, and Transformers like BERT & GPT-3. Recognized
for implementing NLP tools such as SpaCy and Gensim to enhance chatbot and sentiment analysis
functionalities.
Data Engineering Savant who has worked with technologies such as Apache Spark, Apache Hadoop,
and Apache Kafka. Well-versed with SQL & NoSQL databases, and adept at using cloud platforms like
AWS, GCP, and Microsoft Azure.
MLOps & DevOps Enthusiast proficient in using Docker, Kubernetes, and Jenkins for streamlined
deployments. Notably experienced in model explainability tools like LIME and SHAP. Industry
Experience spans across e-learning, online marketplaces, and sustainability, leading cross functional
teams and delivering data-driven insights and innovative solutions.
Contact Work History
Address 2020-12 - Head of Machine Learning & Data
Cambridge, Massachusetts, Current Science
07305 United States
Self Employed
Phone Led pioneering initiatives in supply chain
PHONE NUMBER AVAILABLE management and quality control processes
E-mail Developed predictive models for demand
EMAIL AVAILABLE fluctuations using advanced machine learning
techniques
Enabled suppliers to optimize inventory levels
Skills and minimize waste through demand
forecastin
Programming & Scripting Implemented sentiment analysis algorithms
Languages: using natural language processing (NLP) to
Python, R, Java, C++, Scala analyze customer feedback
Machine Learning Libraries Provided valuable insights for product
& Frameworks: enhancement and market positioning
TensorFlow Designed and deployed computer vision
Keras solutions for automated food inspection
PyTorch Ensured compliance with regulatory standards
scikit-learn and enhanced product quality assurance
XGBoost measures
LightGBM Employed clustering algorithms to segment
Caffe customer demographics
Theano Tailored marketing strategies and personalized
Deep Learning Techniques: customer experiences based on segmentation
Huggingface transformers, Resulted in improved customer satisfaction
GPT and Gemin and retention rates
Transformers (e.g., BERT, Engineered anomaly detection systems using
GPT-2/3) time series analysis
Generative Adversarial Identified potential risks in the supply chain
Networks (GANs) and preempted disruptions
Autoencoders Mitigated operational challenges through
Natural Language proactive risk identification
Processing (NLP) Tools: Orchestrated scalable data pipelines through
SpaCy the integration of big data technologies
NLTK (Apache Hadoop and Spark)
HuggingFace's Transformers Processed vast volumes of data from diverse
Reinforcement Learning: sources
Q-learning Facilitated data-driven decision-making at
Deep Q Networks (DQN) every stage of the food supply chain
Policy Gradient Methods Strategic approach to leveraging
Proximal Policy Optimization cutting-edge technologies in machine
(PPO) learning and data science
Time Series Analysis: Optimized operational efficiency and
SARIMA positioned food suppliers at the forefront of
ARIMA innovation
Prophet Drove sustainable growth and competitive
LSTM for time series advantage in the industry.
Model Interpretability &
Explainability Tools:
2017-06 - Mid-Level: Machine Learning Engineer
2020-10 Devbatch
LIME
SHAP Head of Machine Learning & Data Science for
ELI5 a leading food supplier with a strong
Data Manipulation & e-commerce presence since December 2020
Analysis: Instrumental in revolutionizing market analysis,
Pandas inventory management, and customer
NumPy engagement
Dask Strategically applied machine learning and
Data Visualization: data science to harness insights from vast
Matplotlib datasets
Seaborn Optimized supply chain and personalized the
Plotly customer shopping experience
Tableau Pioneered the development of predictive
Big Data Technologies: models for accurate demand forecasting,
Apache Spark (with MLlib) reducing wast
Apache Hadoop Ensured inventory alignment with consumer
Apache Kafka needs through effective demand predictio
Database & Storage Utilized advanced algorithms to enhance
Systems: recommendation systems for individual
SQL databases (e.g., customer preferences
MySQL, PostgreSQL) Improved sales and customer loyalty through
NoSQL databases (e.g., more intuitive and responsive
MongoDB, Cassandra) recommendation systems
Cloud storage solutions Worked on improving operational efficiencies
(e.g., AWS S3, Google through the automation of key processes
Cloud Storage) Automated pricing strategies and logistics
Cloud Platforms: planning using state-of-the-art AI technologies
AWS (e.g., SageMaker, EC2, Leveraged TensorFlow and PyTorch to build
Lambda) and deploy models predicting and adapting
Google Cloud Platform to market trends in real-time
(e.g., AI Platform, Compute Ensured the e-commerce platform remained
Engine) competitive and responsive to market
Microsoft Azure (e.g., Azure dynamics
Machine Learning) Strategic integration of machine learning into
DevOps & MLOps: the e-commerce framework
Databricks Pivotal role in driving growth, enhancing
Docker customer satisfaction, and establishing the
Kubernetes brand as a leader in the digital food supply
Jenkins sector.
GitLab CI/CD
MLflow
2014-10 - Entry-Level: Machine Learning
2017-05 Developer
Sagemaker
Optimization Algorithms: Mindstorm Studios
Gradient Descent variants Machine Learning Engineer in the
Evolutionary algorithms e-commerce sector of the oil refinery industry
Bayesian optimization from June 2017 to October 2020
Experimentation & A/B Pivotal role in driving innovation through the
Testing: application of machine learnine
Design of Experiments Enhanced customer experiences and
Multivariate testing streamlined operations in the e-commerce
Hypothesis testing sector
Regularization Techniques: Developed sophisticated algorithms for
L1 & L2 regularization predictive analytics
Dropout Significantly improved inventory management
Early stopping by accurately forecasting demand for
Ensemble Methods: petroleum products
Bagging (e.g., Random Notable reduction in surplus inventory and
Forests) ensured product availability aligned with
Boosting (e.g., AdaBoost, market needs
Gradient Boosting) Stacking Worked on dynamic pricing strategies using
machine learning models
Enabled real-time pricing adjustments based
on global oil market trends and local demand
fluctuations
Maximized revenue and ensured competitive
pricing through dynamic pricing strategies
Employed natural language processing (NLP)
techniques to analyze customer feedback
and inquiries
Enhanced the customer support system by
automating responses and identifying areas
for improvement
Expertise in Python and proficient use of
machine learning libraries such as TensorFlow
and scikit-learn
Crucial role in the rapid development and
deployment of machine learning models
Contributions instrumental in transforming thee-
commerce platform into a more adaptive,
efficient, and customer-centric operation
Significant impact on customer satisfaction
and operational efficiency in the digital
domain of the oil refinery sector.
Education
Bachelor of Science: Computer
Science
Virtual University
|
