KEVIN RUSHING
DATA SCIENTIST & AI ENGINEER
Architecting intelligent data ecosystems where engineering rigor meets predictive intelligence. I design ML-native platforms that transform raw signal into automated foresight — shipping Prophet models, Snowpark pipelines, and AI-driven decision systems at scale for NielsenIQ.
IMPACT METRICS
0
ROWS MODELED
7-DAY
FORECAST HORIZON
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YRS BUILDING DATA SYSTEMS
CORE SYSTEMS
MISSION LOG
NielsenIQ
2018 - PRES
Senior Data Scientist & AI Engineer
- Designed and deployed Prophet-based time-series forecasting models natively in Snowpark — imputing missing market data across 100M+ row datasets with production-grade accuracy and zero external infrastructure.
- Architected the enterprise-wide unified data ingestion framework for the MMR organization, consolidating fragmented streams into a single, ML-ready analytical layer that accelerated model development cycles by weeks.
- Built end-to-end Python automation pipelines in Snowflake that cut data delivery latency by 7 days while expanding downstream coverage and model readiness for predictive workloads.
- Pioneered the team's evolution from reactive ETL to intelligence-first data workflows — embedding predictive logic, anomaly detection, and automated feature engineering directly into the data platform.
- Engineered AI-augmented data quality systems that autonomously detect statistical anomalies and flag data drift before it reaches production models.
UAMS
2016 - 2018
Data Systems Engineer
- Re-architected legacy donor database systems into a modern, query-optimized analytics platform serving institutional research, reporting, and early-stage predictive initiatives.
- Designed and automated ETL pipelines with Python, eliminating manual QA bottlenecks and reducing data latency by 80% — laying groundwork for data-driven decision-making at scale.
- Introduced programmatic data validation layers that became the institutional standard for cross-departmental data integrity and downstream model trust.
UALR
2014 - 2016
Systems & Data Engineer
- Managed enterprise database systems and built foundational data engineering practices — designing the reliable data infrastructure that later enabled analytical and ML workloads at scale.
ACTIVE VECTORS
Current research and build focus:
ML OPS
FORECASTING
AI AGENTS
ALPINE
Base of Operations: Colorado Rockies