- Supervised & Unsupervised Learning | Time Series Forecasting | Anomaly Detection

- Deep Learning (TensorFlow, PyTorch) | NLP (Transformers, BERT, GPT)

- Financial Data Modeling | Risk & Fraud Detection | Real-time Data Processing (Kafka, Spark)

- Model Optimization & Deployment (MLflow, Docker, Kubernetes) | SQL & NoSQL Databases

- Cloud Platforms (AWS SageMaker, GCP AI) | API Development & Integration

**Soft Skills:**

- Analytical Thinking & Problem Solving | Stakeholder Communication | Cross-Functional Collaboration

- Agile Methodologies | Continuous Learning | Ethical AI & Explainability Focus

*Lead Machine Learning Engineer*

**FinEdge Solutions, New York, NY** | July 2022 – Present

- Architected a real-time fraud detection system utilizing Graph Neural Networks, reducing unauthorized transactions by 35%.

- Led a team of 4 data scientists and engineers to develop credit scoring models with XGBoost and deep learning techniques, resulting in a 10% lift in approval accuracy.

- Implemented explainability modules integrating SHAP and LIME for regulatory compliance and stakeholder transparency.

- Optimized ML pipelines on AWS, improving training times by 40% while maintaining model robustness.

*Machine Learning Engineer*

**CashFlow Fintech, San Francisco, CA** | August 2018 – June 2022

- Developed predictive models for loan default risk, incorporating NLP on customer communication logs, improving early warning signals.

- Deployed scalable recommendation systems for personalized financial advice across mobile platforms, increasing user engagement by 20%.

- Collaborated with product teams to integrate ML models into banking apps using REST APIs, ensuring seamless deployment and monitoring.

- Enhanced anomaly detection with autoencoders, reducing instances of suspicious activity undetected by traditional methods.

*Data Scientist (Intern)*

**Global Bank Corp., New York, NY** | June 2017 – August 2017

- Assisted in feature engineering and model validation for credit risk models adopting logistic regression and random forests.

- Conducted exploratory data analysis on transaction data to identify subtle fraud patterns.

**Master of Science in Data Science**

Columbia University, New York, NY | 2015 – 2017

**Bachelor of Science in Computer Engineering**

University of California, Berkeley, CA | 2011 – 2015

- Certified Machine Learning Specialist – AWS (2023)

- Ethical AI & Responsible Data Use – Stanford Online (2024)

- Financial Data Analysis & Modeling Certification – CFA Institute (2022)

- **Automated Credit Risk Scoring Platform:** Designed an end-to-end system incorporating NLP and ensemble models, now used by the client’s loan origination team, reducing assessment time by 50%.

- **Real-time Transaction Monitoring Dashboard:** Built with Kafka and Spark Streaming, providing fraud alerts and reporting for compliance in under 2 seconds.

- **Explainability Framework for Risk Models:** Developed a standardized explainability toolkit leveraging LIME and SHAP to meet evolving regulatory standards across multiple financial products.

- Python (scikit-learn, pandas, NumPy) | R | SQL | Spark | Kafka | Docker | Kubernetes | TensorFlow | PyTorch | MLflow | AWS SageMaker | GCP AI | Tableau

- English (Native)

- Spanish (Professional Proficiency)