Miguel Solis Orozco

Sr. ML Engineer and Data Scientist

Phone: (650) 292 – 2455 Email: advdd7@r.postjobfree.com

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Professional Summary

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●Senior Machine Learning Engineer and Data Scientist and Certified Associate in Project Management with close to 9 years of experience in the Data Science, AI, and Machine Learning field.

●Academically proficient with a Master’s in Data Science from Instituto de Educacion Superior de Occidente; versatile technocrat with hands-on experience in Artificial Intelligence, Deep Learning, Machine Learning Frameworks, programming languages, optimization techniques, statistical methods, data systems, python libraries, IDE's, development tools, supervised as well as unsupervised learning.

●Hands-on experience with multiple NLP methods for information extraction, topic modeling, parsing, and relationship extraction

●Hands-on experience in the Computer Vision domain including object detection, image segmentation, and event detection in video

●Well-versed in designing, developing, and deploying custom BI reporting dashboards using Shiny, Shiny dashboard, and Plotly to provide actionable insights and data-driven solutions.

●Experience with neural network architectures such as CNN, R-CNN, YOLO, and GAN.

●Dexterous in the application of statistical learning methods including Regression Analysis, Forecasting, Decision Trees, Random Forest, Classification, Cluster Analysis, Support Vector Machines, and Naive Bayes techniques, AI, ML, Deep Learning, CNN, RNN.

●Brilliant in applying statistical analysis and machine learning techniques to live data streams from big data sources using Spark and Scala; possess cloud platform experience using AWS, GCP, and Azure.

●Demonstrated excellence in transforming business concepts and needs into mathematical models, designing algorithms, and deploying custom business intelligence software solutions; knowledge of building models with deep learning frameworks such as TensorFlow, PyTorch, and Keras.

●An assertive team leader with strong aptitude in developing, leading, hiring, and training highly effective work teams; strong analytical skills with proven ability to work well in a multi-disciplined team environment and adept at easily learning new tools and processes.

Technical Skills

Deep Learning:

Recurrent Neural Networks, LSTM Networks, Artificial Neural Networks, Transfer Learning, Convolutional Neural Networks, Segmentation, Auto encoding/decoding

Programming Languages:

Python, R, MATLAB, Linux, Latex

Optimization Techniques:

Dynamic Programming, Convex Optimization, Non-Convex Optimization, Linear Programming, Monte Carlo Methods, Network Flows

Statistical Methods:

Bayesian Statistics, Hypothesis Testing, Factor Analysis, Stochastic Modelling, Factorial Design, ANOVA

Data Systems:

AWS (RDS, RedShift, Kinesis, EC2, EMR, S3), MS Azure, SQL, MySQL, NoSQL, Spark, Hive, Hadoop

IDEs:

Spyder, Jupyter, PyCharm, RStudio, Eclipse

Machine Learning Frameworks:

TensorFlow, PyTorch, PyTorch, Keras, Caffe

Unsupervised Learning:

Gaussian Mixture Models, K-means Clustering, Hierarchical Clustering, Centroid Clustering, Principal Component Analysis, Singular Value Decomposition

Supervised Learning:

Naive Bayes, Linear Regression, Logistic Regression, ElasticNet Regression, Multivariate Regression, Support Vector Machines, K-Nearest Neighbours, Decision Trees, Random Forests, Natural Language Processing, Time Series Analysis, Survival Analysis

Python Libraries:

TensorFlow, NumPy, Pandas, SciPy, Matplotlib, sci-kit-learn, Keras, PyTorch, PyBrain, Caffe, NLTK, StatsModels, Seaborn, Selenium

Development Tools:

GitHub, Git, IPython notebook, Trello, SVN

Professional Experience

Senior Machine Learning Engineer (MLOps) : Deloitte (Remote) Mar 2020 - Present

Worked as a Senior ML Engineer with a large e-commerce client. I led a team that created and deployed models that can segment customers into different groups based on their behavior and demographics for targeted marketing and promotions. Also designed, implemented, and deployed state-of-the-art natural language processing (NLP) models to create a chatbot for enhancing customer support.

Experience with designing, implementing, and deploying state-of-the-art AI/ML models for customer segmentation

Experience with unsupervised learning algorithms such as K-Means, DBSCAN, Hierarchical Clustering, etc.

Experience with state-of-the-art AI NLP architectures such as BERT, GPT-2, etc. for natural language understanding and generation.

Designed and implemented a chatbot for an e-commerce platform using GPT and AWS technologies

Implemented a continuous integration and deployment (CI/CD) pipeline using AWS CodePipeline and AWS CodeBuild

Conducted model optimization and tuning using AWS SageMaker Hyperparameter Tuning

Managed data collection, preprocessing, and storage using AWS S3 and AWS Glue

Deployed the chatbot model using AWS SageMaker Endpoints and monitored its performance using AWS CloudWatch

Ensured security and compliance by implementing AWS IAM and monitoring AWS CloudTrail and AWS Config

Developed and executed data pipelines and workflows to support the training, validation, and deployment of the chatbot model

Worked with stakeholders to understand requirements and develop an MLOps strategy to meet business objectives

Knowledge of regulatory compliance and data privacy laws, such as GDPR and CCPA.

Experience with AWS security services such as IAM, KMS, and Secrets Manager to secure the models and data.

Strong understanding of AI / NLP concepts, such as text pre-processing, feature extraction, and model fine-tuning.

Strong knowledge of MLOps best practices, including model versioning, monitoring, and scaling in AWS

Strong understanding of data science and machine learning concepts, such as supervised and unsupervised learning, and deep learning

Experience with CI/CD pipeline for machine learning such as Jenkins, GitHub Actions, and CodePipeline, to automate the deployment of models and ensure faster time-to-market.

Experience with model management and registry tools such as MLflow, Databricks, and Hypermodel to manage the lifecycle of AI / ML models and enable collaboration among teams.

Followed Agile methodology and used tools such as JIRA and Confluence

Developed and executed data pipelines and workflows to support the training, validation, and deployment of the chatbot model, using Agile project management tools

Data Scientist & ML Engineer : CareStream Health Apr 2018 - Feb 2020

Built a semantic segmentation model to correctly measure femoral cartilage in X-ray images of knee joints.

Experience with designing, implementing, and deploying state-of-the-art computer vision-based semantic segmentation models for X-ray images

Experience with state-of-the-art AI/ML models, deep learning, and image segmentation architectures such as U-Net, Mask R-CNN, and DeepLab V3 for medical image analysis

Evaluated the performance of the computer vision-based image segmentation model using metrics such as Intersection over Union (IoU), Dice Similarity Coefficient (DSC), Precision, Recall, F1-score, and Mean Average Precision (mAP).

Implemented MLOps best practices, including model versioning, monitoring, and scaling in the AWS cloud

Designed and implemented an image preprocessing pipeline using Amazon Simple Storage Service (S3) for image storage and retrieval.

Utilized Amazon Elastic Compute Cloud (EC2) instances for running image preprocessing tasks, ensuring scalability and computing capacity.

Deployed image preprocessing tasks in a containerized environment using Amazon Elastic Container Service (ECS) or Amazon Elastic Kubernetes Service (EKS).

Automated image data extraction, preprocessing, and loading using AWS Glue, resulting in efficient and streamlined image preprocessing operations.

Monitored and managed image processing tasks using Amazon CloudWatch, ensuring smooth and efficient operations.

Experience with state-of-the-art semantic segmentation architectures such as U-Net, Mask R-CNN, and DeepLab V3 for medical image analysis

Utilized SageMaker's flexible and customizable architecture to build, train, and deploy deep learning models.

Utilized SageMaker's integration with TensorFlow to build, train, and deploy deep learning models, reducing development time and resources.

Monitored and managed deep learning models using Amazon CloudWatch, ensuring optimal performance and accuracy.

Deployed deep learning models in a containerized environment using Amazon Elastic Container Service (ECS) or Amazon Elastic Kubernetes Service (EKS) for improved scalability and resource management.

Implemented CI/CD pipelines using AWS CodePipeline and AWS CodeBuild, ensuring efficient and streamlined deployment processes.

Continuously fine-tuned and updated the models using Amazon SageMaker, ensuring optimal performance and accuracy over time.

Experience with A/B testing, experimentation, and statistical analysis in order to measure and optimize the performance of the models

Strong background in software development and experience with Git, JIRA, and other development tools.

Experience with data visualization tools such as Tableau, PowerBI, or Looker to present the performance and insights of the models

Strong communication skills with the ability to explain technical concepts to non-technical stakeholders

Data Scientist: PNC Financial Services – Pittsburgh, PA Jun 2016 - Mar 2018

Developed a computer vision OCR model for automation of the data entry process for scanned pdf documents and creating a queryable database. Experienced in designing, developing, and deploying machine learning models for fraud detection and risk analytics.

Developed an OCR model using deep learning computer vision models like CNN, Tesseract

Applied grid-search, ensembling of different AI/ML models, deep learning-based layers of LSTMs, etc. to enhance model performance

Pytesseract and OpenCV packages were used for the OCR system

Strong knowledge of statistical and machine learning techniques, such as anomaly detection, clustering, and classification.

Strong expertise in data visualization and data exploration tools such as Tableau, PowerBI, and Looker, as well as Azure services like Power BI and Azure Databricks for data analytics and visualization.

Proficient in programming languages such as Python and R, and familiar with machine learning libraries such as sci-kit-learn, TensorFlow, and PyTorch

Strong understanding of data science and machine learning concepts such as supervised and unsupervised learning and deep learning

Experience with distributed computing frameworks such as Apache Spark

Strong background in data analytics and data science and experience with Git, JIRA, and other development tools.

Strong communication skills with the ability to explain technical concepts to non-technical stakeholders

Experience leading teams or projects in a data science capacity, specifically within the banking industry.

Experience with A/B testing, experimentation, and statistical analysis to measure and optimize the performance of ML models for fraud detection and risk analytics.

Knowledge of regulatory compliance and data privacy laws, such as GDPR and CCPA.

Experience with risk analytics techniques such as credit scoring, stress testing, and portfolio optimization.

Familiar with state-of-the-art techniques for fraud detection such as graph-based approaches, and adversarial training.

Experience with explainability and interpretability techniques such as LIME, SHAP, and integrated gradients to understand the decision-making process of deep learning models.

Experience with CI/CD pipeline for machine learning such as Azure DevOps, GitHub Actions, or Jenkins to automate the deployment of models and ensure faster time-to-market.

Experience with model management and registry tools such as Azure Machine Learning Model Management to manage the lifecycle of ML models and enable collaboration among teams.

Data Scientist: Softtek – Addison, TX Jun 2014 - May 2016

As a Data Scientist for Softtek, I worked in the customer experience domain and solved problems related to customer profiling, product recommendation, and customer churn for subscription services.

Strong knowledge of SQL and experience with data manipulation and extraction from various data sources.

Strong expertise in data visualization and data exploration tools such as Tableau, PowerBI, Looker, and Dash for creating interactive dashboards and visualizing the results of the models.

Proficient in programming languages such as Python and R, and familiar with machine learning libraries such as sci-kit-learn, TensorFlow, and PyTorch

Strong understanding of data science and machine learning concepts such as supervised and unsupervised learning, deep learning, natural language processing, and traditional ML models

Experience with data preparation and feature engineering techniques to improve model performance.

Strong background in data analytics and data science and experience with Git, JIRA, and other development tools.

Strong communication skills with the ability to explain technical concepts to non-technical stakeholders

Experience leading teams or projects in a data science capacity, specifically within the subscription service industry.

Experience with A/B testing, experimentation, and statistical analysis to measure and optimize the performance of ML models for customer profiling, product recommendation, and customer churn prediction.

Experience with various recommendation models such as collaborative filtering, content-based filtering, and matrix factorization.

Experience with customer churn prediction using techniques such as survival analysis and deep learning-based models.

Experience with defining, tracking, and analyzing key performance indicators (KPIs) such as customer retention rate, customer lifetime value, and purchase prediction accuracy to measure the effectiveness of the models.

Educational Credentials

Master’s in Data Science

Instituto de Educación Superior de Occidente

Bachelor’s in Computer Science

Instituto de Educación Superior de Occidente

Bachelor’s in Business Administration

Instituto de Educación Superior de Occidente

Certifications

Certified Associate in Project Management, Responsive Web Design

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