Data Scientist Senior

Angel Contreras

Senior Data Scientist

*****************@*****.*** 432-***-**** Dallas, TX

linkedin.com/in/angel-contreras-121542354/

Senior Data Scientist with 8 years of experience building and deploying machine learning and AI solutions at scale. Strong expertise in predictive modeling, deep learning, NLP, recommendation engines, and experimentation. Skilled in Python, R, SQL, and Spark with practical experience across AWS, GCP, and Azure. Hands-on with modern ML and MLOps tools including TensorFlow, PyTorch, Scikit-learn, Hugging Face, MLflow, and LangChain. Known for delivering measurable impact by combining statistical analysis, advanced modeling, and scalable production systems. SKILLS

● Languages: Python, R, SQL, Scala, Java, C/C++, Bash

● Machine Learning & AI: Scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers, OpenCV, XGBoost, LightGBM, NLP, Time Series, Recommendation Systems, Deep Learning, LLMs, LangChain, RAG, MLflow, DVC

● Data Science & Analytics: Pandas, NumPy, SciPy, Statsmodels, dbt, Great Expectations, Excel, Google Sheets, Microsoft Office, A/B Testing, Hypothesis Testing, Regression, Bayesian Methods, Causal Inference

● Data Storage & Platforms: Snowflake, Redshift, BigQuery, PostgreSQL, MongoDB, ElasticSearch, AWS S3, DynamoDB

● Big Data & Streaming: Apache Spark, Kafka, Flink, Beam

● Visualization & BI: Tableau, PowerBI, Matplotlib, Seaborn, Grafana, Superset

● Cloud & MLOps: AWS SageMaker, GCP AI Platform, Azure ML, Databricks, Docker, Kubernetes, Terraform, GitHub Actions, Jenkins

EXPERIENCE

Senior Data Scientist - AstroSirens Mar 2022 - Present

● Designed and deployed customer churn prediction and lead scoring models using XGBoost, PyTorch, and SageMaker. Increased upsell conversion by 18% year over year and reduced churn risk for high-value accounts.

● Built a generative AI contract review pipeline using GPT-4 and LangChain with embeddings stored in ElasticSearch. Helped legal operations reduce review cycles by 70% while maintaining compliance accuracy.

● Developed a demand forecasting framework using LSTM and Prophet models in Databricks. Improved forecasting accuracy by 25% and optimized supply chain planning.

● Applied clustering methods like K-means and DBSCAN to create dynamic customer segments. Integrated segments into AWS-based pipelines for personalized marketing campaigns.

● Ran large-scale A/B experiments to measure product feature effectiveness. Applied uplift modeling and delivered actionable results that influenced roadmap decisions.

● Integrated MLflow with SageMaker for model versioning, training, and deployment. Built CI/CD pipelines with GitHub Actions to automate retraining and testing.

● Designed and delivered PowerBI, Tableau and Excel dashboards that combined predictive insights with near real-time product usage metrics and NPS trends for leadership teams.

● Created real-time fraud detection models leveraging streaming data from Kafka and Spark. Reduced incident response times by 40% and lowered false positives.

● Mentored junior data scientists and engineers on feature engineering, deep learning, and experiment design. Conducted peer reviews and hands-on workshops to raise team capability. Machine Learning Engineer - Databricks Aug 2018 - Feb 2022

● Built predictive maintenance models for IoT data streams using Gradient Boosting and Random Forest. Deployed pipelines in Azure ML and helped clients reduce equipment downtime by 20%.

● Developed anomaly detection models with PyTorch autoencoders on streaming data from Kafka. Enabled real-time alerts that improved reliability of system monitoring.

● Created a modular feature store with Snowflake and dbt to support reuse across multiple ML projects. Reduced new model development time by 60%.

● Built recommendation models using collaborative filtering and matrix factorization. Delivered personalized product suggestions that drove measurable improvements in engagement.

● Led A/B testing initiatives across product features, applying statistical methods and causal inference techniques to guide product decisions with confidence.

● Developed NLP pipelines with spaCy and Hugging Face Transformers to extract entities and relationships from unstructured text. Supported compliance and knowledge management teams.

● Built time-series forecasting models for revenue planning using Prophet and ARIMA. Achieved 22% improvement in forecast accuracy over baseline methods.

● Deployed real-time personalization APIs using FastAPI and Kubernetes, exposing model predictions to web and mobile apps.

● Partnered with fraud detection teams to design classification models using ensemble methods and logistic regression. Cut false negatives by 15% and reduced fraud losses.

● Created data lineage visualization tool combining Flask and React, enabling governance and audit teams to trace data usage across pipelines.

Data Science Intern - Netflix Nov 2017 - May 2018

● Built and tested ETL and modeling pipelines in PySpark and Scikit-learn to process millions of playback and search events. Supported ranking and recommendation experiments for content discovery.

● Designed regression models to study key drivers of session completion and shared results with product teams to refine personalization strategies.

● Developed Tableau dashboards that highlighted daily active users, session completion rates, and A/B test outcomes. Improved visibility into engagement metrics.

● Applied NLP methods to classify and cluster catalog metadata, improving discoverability of titles in search and browse features.

● Assisted senior engineers in optimizing S3 partitioning and Redshift schemas for analytics and modeling. Reduced query times significantly for multiple reporting use cases.

● Containerized experimental jobs with Docker and gained hands-on exposure to production workflows, model governance, and best practices in machine learning systems. EDUCATION

Master of Science in Engineering Data Science & AI University of Houston Jun 2015 - Oct 2017 Houston, TX

Bachelor of Science in Computer Science University of Houston Apr 2011 - May 2015 Houston, TX

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