Data Science | Machine Learning | Data Engineering

Core Competencies

Programming Languages: Python, JavaScript, SQL, R
ML and Analytics: TensorFlow, PyTorch, scikit-learn, EconML, Tableau, Power BI, Seaborn, ggplot2
Data Engineering: Apache Spark, Apache Hive, Apache Airflow, Trino, BigQuery, Snowflake, AzureSQL
Infrastructure and CI/CD: AWS, Azure, Github Actions, Docker, Cloudformation, Bicep

Designed and implemented an on-line lead scoring ML system, resulting in a 67% improvement in call targeting accuracy
Created automated system for Google Ads Campaign management, saving $20K monthly and increasing lead quality
Deployed outbound AI appointment setting system, generating $30K in additional monthly revenue
Established and implemented DevOps and MLOps best practices, including automated deployments, rollbacks, retraining workflows, and performance monitoring, streamlining ML and application development
Launched an internal RAG application for document retrieval and summarization, reducing supervisor-to-agent ratio and time-to-sale, resulting in significant payroll savings and increased revenue
Audited third party software development vendors for timelines and cost
Internalized MuleSoft integration capabilities, reducing feature development time and eliminating rework

Architected and implemented business data model by translating logical domain requirements into technical design specifications
Designed and implemented a scalable data warehouse architecture
Develop and support custom data integration pipelines for ingesting data from third-party systems
Create and support automated executive dashboards aligning technical metrics with business objectives
Contributed to SOC 2 certification efforts, implementing security policies and controls within AWS

Independently developed trade secret methodology to enhance Nielsen validation quality and automate manual processes, eliminating manual weighting and behavioral psychologist involvement, saving 20 hours per iteration
Developed team utility platform using React/Flask, including custom-built labeling interface, consolidating multiple tools, reducing testing time by 60%, and reducing time to label by 90%
Engineered critical ML models with MLOps system utilizing Evidently, substantially reducing operational overhead
Led cross-functional product initiatives through data analysis and validation, collaborating with engineering teams to improve product quality and testing processes
Contributed documentation to facilitate onboarding of new hires onto the custom development platform.

Initiated ML business science practice, leading to new internal consulting service
Designed and implemented an end-to-end ML pipeline for client, enabling predictive sales analytics
Served as an internal consultant for client sales and marketing teams, driving a 200% increase in sales engagement through data-driven strategies and insights
Performed diverse technical roles: BI development, ETL, data analysis, and business systems analysis
Promoted within 4 months of starting position
Translated business requirements into technical solutions through cross-team collaboration

Manage end-to-end tech event planning including speaker outreach, venue coordination, and day-of execution