SENIOR DATA SCIENTIST | PRODUCT ANALYTICS & EXPERIMENTATION
Kyle
Kaufman
Building ML systems that power product decisions through A/B testing, causal inference, and real-time metrics.
Full-Stack Data Science
React/Next.js, FastAPI/Flask, PostgreSQL/MongoDB with Docker/Kubernetes orchestration for production ML platforms
DevOps & Containerization
Docker multi-stage builds, Kubernetes (EKS/GKE), Helm charts, ArgoCD with automated CI/CD pipelines
Cloud-Native Architecture
AWS (SageMaker, EKS, Lambda) & GCP (Vertex AI, GKE, Cloud Run) with microservices and event-driven design
TECHNICAL EXPERTISE
About Me
Full-Stack Data Scientist
I build production ML systems from concept to deployment. My work spans the full data science lifecycle—from architecting data pipelines and training models to deploying scalable APIs and building interactive dashboards that drive business decisions.
At KBR, I lead data science initiatives including predictive maintenance systems achieving 92% accuracy and NLP pipelines processing 10K+ documents weekly. Previously at Ford Motor Company, I built enterprise data platforms serving 500+ engineers and won an internal hackathon for an NLP-powered data discovery chatbot.
I'm also the creator of DataFlowHub.AI, a full-stack ML platform featuring FRED API integration with 800K+ economic datasets, GPT-4 powered analytics, and a PostgreSQL data warehouse with star schema architecture.
Technical Strengths
Machine Learning & NLP
Building and deploying ML models (XGBoost, neural networks, ensemble methods) and NLP systems using OpenAI GPT-4, LangChain, and custom entity recognition pipelines achieving 89%+ accuracy.
Data Engineering
Designing end-to-end data pipelines with Apache Spark, Kafka, and Airflow. Building data warehouses with dimensional modeling, ETL automation, and query optimization achieving 10x performance improvements.
Cloud & DevOps
Deploying on AWS (SageMaker, EKS, Lambda) and GCP (Vertex AI, BigQuery, GKE) with Docker, Kubernetes, and CI/CD pipelines. Google Cloud Professional Data Engineer certified.
Awards, Certifications & Publications
Professional Certifications
Google Cloud Professional Data Engineer
Google Cloud Platform
Certified in designing, building, and operationalizing data processing systems on GCP including BigQuery, Dataflow, Vertex AI, Cloud Functions, and Pub/Sub
Awards & Certifications
Modernizing Everywhere Award
Ford Motor Company • December 2022
Recognized by Cynthia Gumbs for leadership and engagement in the Data Discovery IBM Watson Knowledge Catalog Proof of Concept, a key strategic deliverable for Ford+ Plan modernization initiatives
Create Must-Have Products and Services Award
Ford Motor Company • July 2022
Recognized by Jayant Manerikar for exceptional work with Informatica 10.5 Upgrade, ensuring successful implementation and delivery of critical enterprise systems
Ford GDIA Hackathon Winner
Ford Motor Company • 2023
Won internal hackathon for developing NLP-powered data discovery chatbot using Vertex AI and LangChain. Prototype translated natural language queries to SQL across PostgreSQL and BigQuery, demonstrating 85% time-to-insight reduction for non-technical users
Machine Learning Projects & Case Studies
DataFlow Hub.AI
Enterprise ML SaaS Platform • www.dataflowhub.ai
Architected and deployed production-grade ML platform from concept to deployment. Full-stack implementation featuring FRED API integration (800,000+ economic datasets), context-aware GPT-4 AI assistant, and PostgreSQL data warehouse with OLTP/OLAP architecture. Orchestrated with Docker Compose (15+ services) and deployed on cloud infrastructure.
TECHNICAL ARCHITECTURE
- FRED API Integration: TypeScript client enabling search/import of 800,000+ Federal Reserve economic datasets with advanced filtering, auto-conversion, and one-click import workflow
- Enhanced AI Chat: Context-aware GPT-4 assistant adapting to user profiles (industry, use case, objectives) for personalized data science guidance across 10+ industry verticals
- PostgreSQL Data Warehouse: Dual-database OLTP/OLAP architecture with star schema dimensional modeling, ETL pipelines via Supabase Edge Functions, achieving 10x query optimization
- Full-Stack: React/TypeScript frontend, FastAPI backend with async workers, PostgreSQL + Redis caching, REST API with OAuth 2.0 authentication
- Containerization: Docker Compose with 15+ services deployed on AWS EKS, achieving 99.9% uptime with horizontal auto-scaling and CI/CD via GitHub Actions
FRED Economic Data Integration
Federal Reserve API • TypeScript Client • One-Click Import
Engineered a comprehensive FRED (Federal Reserve Economic Data) integration enabling search and import of 800,000+ economic datasets. Built complete TypeScript API client with fuzzy search, advanced filtering (date range, frequency, units), and automatic data conversion. Features professional search UI with autocomplete, popular indicator quick-access (GDP, unemployment, CPI), and one-click import workflow.
KEY FEATURES
- 800,000+ Economic Series: GDP, inflation, unemployment, interest rates, housing, and financial market data from the Federal Reserve
- Advanced Filtering: Date range selection, frequency options (daily to annual), and unit transformations (levels, percent change, YoY)
- Auto-Conversion: Automatic conversion to platform format with metadata extraction and intelligent dataset tagging
Context-Aware AI Data Assistant
Personalized Guidance • 10+ Industry Verticals • Real-time Streaming
Developed an intelligent AI chat assistant that provides personalized data science guidance by leveraging user context. The system loads user profiles from Supabase and dynamically generates contextual system prompts for GPT-4, delivering industry-specific recommendations across business analytics, healthcare, finance, marketing, and more.
KEY FEATURES
- User Profile Context: Adapts to profession, industry, use case, and objectives stored in Supabase for personalized responses
- 10+ Industry Verticals: Specialized guidance for business analytics, fraud detection, healthcare, finance, marketing, supply chain, and more
- Dataset-Aware Analysis: Understands uploaded datasets and provides specific recommendations based on data structure and content
PostgreSQL Data Warehouse Architecture
OLTP/OLAP Separation • Star Schema • ETL Pipelines
Architected a production-grade PostgreSQL data warehouse implementing industry-standard dimensional modeling. Designed dual-database strategy separating operational (Supabase OLTP) from analytical (PostgreSQL OLAP) workloads, achieving 10x query optimization for BI analytics and real-time dashboards.
KEY FEATURES
- Star Schema Design: Dimension tables (dim_datasets, dim_users) and fact tables (fact_analysis_events, fact_dataset_health, fact_model_performance)
- ETL Pipelines: Supabase Edge Functions with Foreign Data Wrapper (FDW) for cross-database queries and automated data synchronization
- Pre-computed Aggregations: Daily usage stats, user engagement metrics, and model performance trends for instant dashboard loading
REAL-TIME BLOCKCHAIN ANALYTICS
Bitcoin Whale Tracker
ML Price Prediction • Real-Time Monitoring • 11+ API Integrations
Enterprise-grade cryptocurrency analytics platform monitoring Bitcoin whale transactions in real-time. Integrated 11+ external APIs for multi-source data aggregation, ML-powered price predictions using TensorFlow.js achieving 78% accuracy, and automated pattern detection algorithms. Features WebSocket streaming architecture processing 1000+ data points per minute with Docker-orchestrated microservices.
TECHNICAL IMPLEMENTATION
- Real-Time Data Pipeline: WebSocket-based streaming architecture processing Bitcoin blockchain data with <200ms latency using Socket.io and Express middleware
- ML Prediction Engine: TensorFlow.js neural network models trained on 80+ features achieving 78% directional accuracy for 24-hour price forecasts
- Multi-Source Aggregation: Orchestrated 11+ external APIs (CoinGecko, FRED, NewsAPI, CryptoCompare, Alpha Vantage) with intelligent rate limiting and caching strategies
- Pattern Detection: Proprietary algorithms identifying market patterns (accumulation, distribution, consolidation) with 85%+ confidence scoring using statistical analysis
- Microservices Architecture: Docker Compose orchestration with PostgreSQL database, Prisma ORM for type-safe queries, and automated background job scheduling
TIME SERIES FORECASTING
Multi-Model Ensemble Trading System
LSTM + XGBoost + Prophet • 98.2% Accuracy
Advanced ensemble forecasting system combining LSTM neural networks, XGBoost, and Prophet models for stock market predictions with 98.2% accuracy, $2.01 RMSE, and 96% confidence intervals for risk assessment. Real-time trading dashboard with WebSocket integration processing 50K+ ticks per second.
TECHNICAL IMPLEMENTATION
- Ensemble Architecture: Weighted combination of LSTM (40%), XGBoost (35%), and Prophet (25%) for optimal predictions
- Microservices: 8 Docker containers (WebSocket API, LSTM engine, XGBoost service, Redis cache) deployed on AWS EKS with Helm
- Feature Engineering: 50+ technical indicators including moving averages, RSI, MACD, and rolling statistics
- Frontend: React/TypeScript dashboard with TradingView charts, real-time updates via WebSocket, and interactive prediction interface
- Performance: 98.2% accuracy, $2.01 RMSE on 30-day forecasts with 96% confidence intervals and <50ms latency
Developed production NLP pipeline processing 10K+ maintenance reports weekly with 89% entity recognition accuracy, automating manual review processes and saving 120 hours per month.
TECHNICAL IMPLEMENTATION
- Architecture: OpenAI GPT-4 with custom prompt engineering for domain-specific entity extraction
- Dataset: 10K+ weekly maintenance reports with structured and unstructured text
- Performance: 89% entity recognition accuracy, 95% classification precision
- Pipeline: Automated text preprocessing, entity extraction, classification, and structured output generation
Engineered distributed anomaly detection system using Apache Spark and Python ML libraries to process IoT sensor data streams in real-time. The system identifies anomalies using Isolation Forest and Random Forest algorithms with 94.5% accuracy, processing 10K+ sensor readings per second with sub-second latency and automated alerting capabilities.
TECHNICAL IMPLEMENTATION
- ML Algorithms: Isolation Forest (primary detector) with 94.5% accuracy, Random Forest for anomaly classification, and statistical methods (Z-score, IQR) for outlier detection
- Distributed Processing: Apache Spark Structured Streaming for real-time data ingestion, processing 10K+ sensor readings/second with horizontal scalability
- Cloud Infrastructure: AWS S3 data lake architecture, AWS Glue for ETL, containerized with Docker and deployed on Kubernetes with auto-scaling
- Performance: Sub-second processing latency, 94.5% detection accuracy with 2.1% false positive rate, severity-based automated alerting system
Comprehensive ML research project using ensemble methods to predict housing prices with 92% R-squared accuracy across 50K+ property records and 20 metropolitan areas.
TECHNICAL IMPLEMENTATION
- Algorithms: Ensemble of XGBoost, Random Forest, and Neural Networks with stacking
- Dataset: 50K+ property records with 80+ features including economic indicators
- Performance: 92% R-squared, RMSE $18,450, 15% improvement over baseline models
- Feature Engineering: Polynomial features, interaction terms, temporal encoding, and geographic clustering
Enterprise Data Discovery Chatbot
Ford Internal Hackathon • Natural Language to SQL
Won Ford GDIA internal hackathon by building a conversational AI chatbot that translates natural language questions into SQL queries across PostgreSQL and BigQuery databases. The prototype demonstrated democratizing data access for non-technical employees, enabling instant insights without SQL expertise.
HACKATHON IMPLEMENTATION
- NLP Pipeline: Vertex AI LLMs with LangChain for intent extraction, entity recognition, and semantic mapping to database schemas
- Multi-Database Support: Built connectors for PostgreSQL (transactional data) and BigQuery (analytics warehouse) with query optimization
- Prototype Features: Natural language interface, automated SQL generation, role-based access control, and interactive result visualization
- Hackathon Recognition: Selected as winning project for demonstrating measurable time-to-insight improvements and enterprise scalability potential
Technical Expertise
ML/AI
- XGBoost & Gradient Boosting
- Neural Networks (MLP, CNN, LSTM)
- NLP & Transformers
- Time Series Forecasting
- Causal Inference
- A/B Testing & Experimentation
DATA ENGINEERING
- Apache Spark (PySpark)
- Apache Kafka
- Apache Airflow
- ETL Pipelines
- BigQuery & Data Warehousing
- Databricks
FRONTEND
- React & Next.js
- TypeScript
- Tailwind CSS
- D3.js & Recharts
- Responsive Design
- Web Performance
BACKEND
- FastAPI & Flask
- Python
- PostgreSQL & MongoDB
- Redis
- REST & GraphQL APIs
- WebSockets
CLOUD/DEVOPS
- AWS (SageMaker, EKS, Lambda)
- GCP (Vertex AI, BigQuery)
- Docker & Kubernetes
- Terraform & IaC
- CI/CD Pipelines
- GitHub Actions
Get In Touch
Seeking opportunities in machine learning engineering, AI research, and data science roles where I can apply advanced ML techniques to solve complex problems and lead technical teams.
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