Harrison is available for hire
Hire HarrisonHarrison Marick
Verified Expert in Engineering
Data Scientist and Developer
Mount Pleasant, SC, United States
Toptal member since March 1, 2024
Bio
Harrison is a data science and AI professional with experience designing and deploying machine learning and analytics solutions in complex environments. He works across the full data lifecycle, from data engineering and modeling to production deployment, translating complex data into actionable insights and scalable, reliable AI systems aligned with business goals.
Portfolio
Tiber Solutions
Amazon Web Services (AWS), Python 3, Artificial Intelligence (AI), Data Science...
Big League Advantage
Amazon Web Services (AWS), Machine Learning, Artificial Intelligence (AI)
Big League Advantage
Python 3, Amazon Web Services (AWS), Azure, Machine Learning...
Experience
- Python 3 - 10 years
- Data Science - 9 years
- Predictive Modeling - 8 years
- Amazon Web Services (AWS) - 6 years
- Artificial Intelligence (AI) - 6 years
- Machine Learning - 6 years
- Streamlit - 3 years
- OpenAI - 1 year
Preferred Environment
Python 3, Artificial Intelligence (AI), Amazon Web Services (AWS), Cloud Computing, Data Science, Analytics, Large Language Models (LLMs), Machine Learning, Mathematics
The most amazing...
...product I've built offered custom no-code predictive models on a customer-facing app called Rithmm that has tens of thousands of daily active users.
Work Experience
Chief Strategy Officer
2021 - PRESENT
Tiber Solutions
- Led the design and development of a no-code predictive modeling platform for sports analytics, enabling non-technical users to build, test, and deploy models without writing code.
- Built an AI-driven platform to evaluate publicly traded companies by analyzing and synthesizing insights from public financial filings, improving the speed and consistency of investment analysis.
- Architected and delivered a large-scale data warehouse integrating thousands of data sources, forming the foundation of an analytics offering generating over $1+ million in annual recurring revenue.
- Scaled data and AI consulting capabilities by doubling both revenue and team headcount through delivery excellence, client expansion, and strategic hiring.
- Drove end-to-end execution of data science and AI initiatives, translating complex technical solutions into business-ready products adopted by internal teams and external clients.
Technologies: Amazon Web Services (AWS), Python 3, Artificial Intelligence (AI), Data Science, Consulting
Senior Data Scientist
2018 - 2021
Big League Advantage
- Developed predictive machine learning models across several sports that proved profitable when deployed for sports betting.
- Grew revenue and team size as the initial data scientist in a team of over 20 technical colleagues. Hired and managed a team of roughly five team members.
- Led the launch of multiple revenue streams that leveraged our custom-built artificial intelligence products.
Technologies: Amazon Web Services (AWS), Machine Learning, Artificial Intelligence (AI)
Lead Data Scientist
2018 - 2018
Big League Advantage
- Oversaw daily risk management and hedging for a large portfolio of positions in sports prediction markets, optimizing exposure and driving consistent profitability through data-driven decision-making.
- Led and managed a team of data scientists supporting NBA basketball operations, delivering models and analytics that informed player evaluation, strategy, and investment decisions.
- Scaled the data science organization from 3 to 20 team members, establishing hiring standards, technical direction, and delivery processes to support rapid growth.
Technologies: Python 3, Amazon Web Services (AWS), Azure, Machine Learning, Artificial Intelligence (AI), Large Language Models (LLMs), OpenAI, Natural Language Processing (NLP)
Experience
Sentiment Analysis of SEC Documents
This project involved designing an artificial intelligence engine to analyze publicly traded companies' SEC filings and assess their regulatory risk. I built an engine that ingests SEC documents and uses a custom-built artificial intelligence algorithm that leverages NLP and sentiment analysis to evaluate publicly traded companies based on regulatory risk. In addition to the algorithm, I developed the entirety of the back-end infrastructure in AWS (Docker, ECS, Batch) that ran the updated results regularly.
No-code Sports Prediction Platform
https://www.rithmm.com/Built a data-driven sports analytics platform that uses advanced modeling and machine learning to generate predictions, probabilities, and insights across multiple sports and betting markets. The platform is designed to translate complex statistical outputs into clear, actionable recommendations, helping users evaluate risk, identify value, and make more informed decisions in real time.
Secure Artificial Intelligence
http://gocaddie.aiBuilt a secure, enterprise-grade AI platform designed to transform complex and fragmented data into clear, actionable intelligence. The platform combines governed retrieval, advanced language models, and customizable AI agents to help organizations search, analyze, and synthesize information across internal and external data sources. Built for scale and reliability, it enables AI-powered workflows that support decision-making, automation, and insight generation while maintaining strict controls around privacy, security, and data governance.
Education
2013 - 2018
Bachelor's Degree in Mathematics and Statistics
Amherst College - Amherst, MA, USA
Skills
Tools
Spyder
Languages
Python 3
Frameworks
Streamlit
Platforms
MacOS, Amazon Web Services (AWS), Azure
Other
Data Science, Predictive Modeling, Analytics, Machine Learning, Mathematics, Statistics, Consulting, Artificial Intelligence (AI), Natural Language Processing (NLP), OpenAI, Large Language Models (LLMs), Infrastructure, Cloud Computing
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