Miguel Solis Orozco

Sr. ML Engineer and Data scientist.

Phone: (650) 398 – 3038 Email: ads9uz@r.postjobfree.com

Professional Summary

●Senior Machine Learning Engineer and Data Scientist and Certified Associate in Project Management with 8+ 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 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, 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. In Fraud detection, models were deployed that can detect and prevent fraud in online transactions. Designed, implemented, and deployed state-of-the-art natural language processing (NLP) models for customer support.

Experience with designing, implementing, and deploying state-of-the-art machine learning and deep learning models for customer segmentation and fraud detection as an MLOps engineer in an e-commerce setting

Experience with state-of-the-art NLP architectures such as BERT, GPT-2, and ULMFiT for text classification, sentiment analysis, and intent recognition

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

Experience with supervised and unsupervised learning algorithms such as K-Means, DBSCAN, Random Forest, XGBoost, etc.

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

Experience with AWS services such as SageMaker, Lambda, and Glue for building, deploying, and monitoring ML models

Proficient in programming languages such as Python and R

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 TensorFlow, PyTorch, and Apache Spark

Familiarity with containerization and orchestration tools like Kubernetes, Docker

Strong background in software development 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 machine learning or data science capacity, specifically within the e-commerce industry.

Experience with data visualization tools such as QuickSight, CloudWatch, and X-Ray to present the performance and insights of the models and troubleshoot issues.

Experience with A/B testing, experimentation, and statistical analysis in order to measure and optimize the performance of ML models for customer segmentation and fraud detection.

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 the data used.

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 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 ML models and enable collaboration among teams.

Strong understanding of monitoring and troubleshooting tools such as CloudWatch, Loggly, and Prometheus, to ensure the proper functioning of the models in production.

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 semantic segmentation models for X-ray images as an on-premise solution.

Strong knowledge of MLOps best practices, including model versioning, monitoring, and scaling in on-premise environments.

Experience with on-premise tools and technologies such as Kubernetes, OpenShift, and Ansible for building, deploying, and monitoring semantic segmentation models.

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

Proficient in programming languages such as Python

Strong understanding of image processing and computer vision concepts, such as image enhancement, feature extraction, and model fine-tuning.

Experience with distributed computing frameworks such as TensorFlow, PyTorch, and Apache Spark

Familiarity with containerization and orchestration tools like Kubernetes, Docker

Strong background in software development 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 machine learning or computer vision capacity, specifically within the medical imaging industry.

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

Experience with A/B testing, experimentation, and statistical analysis in order to measure and optimize the performance of semantic segmentation models for X-ray images.

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

Experience with on-premise security best practices to secure the models and the data used.

Experience with CI/CD pipeline for semantic segmentation such as Jenkins, GitLab CI/CD, etc. to automate the deployment of models and ensure faster time-to-market.

Experience with model management and registry tools such as MLflow and Databricks

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

Experience in designing, developing, and deploying machine learning models for fraud detection and risk analytics in PNC Financial Services as a Data Scientist. Worked in Fraud detection, default risk analysis, and expense segmentation problems across all product classes in Retail Lending.

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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