Ready to unlock the power of your data?

At Dark Sky Data, we believe in the limitless power of data—and we build tools that make it work for you, fast.

Our products turn messy spreadsheets into instant insight, no models or IT required. Whether you’re tackling cancellations, margin leakage, or marketing waste, our tools show you what’s happening—and what to do next.

We work with VSC and home warranty marketers, administrators, private equity firms, lenders, and equipment finance companies—anyone who needs faster, smarter decisions.

Spend smarter. Retain more. Move faster.

JT Turner brings advanced computational modeling and statistical analysis expertise to Dark Sky Data. With a background in astrophysics and high-performance computing, JT has extensive experience working with large-scale simulations and complex datasets.

JT’s research has included modeling early-universe dynamics through large-scale scalar field simulations, conducting planetary analysis at Lowell Observatory using TESS satellite data, and supporting radio astronomy research with the Green Bank Telescope by detecting and predicting radio frequency interference (RFI) to improve data integrity and collection efficiency, including published work on radio signal analysis. Across these domains, their work centered on extracting signal from high-noise environments and analyzing system behavior over time.

In their coursework, JT simulated quantum computing using Python. Their work can be found here: Python Simulations of a Quantum Computer.

At Dark Sky Data, JT applies this analytical discipline to duration-based modeling, portfolio performance analysis, and structured data evaluation. Their focus is on building reliable analytical frameworks that support data-driven decision-making.

JT earned a Bachelor of Science in Astrophysics from Haverford College, with a minor in Classical and Cultural Studies. They completed the Columbia University Data Analytics Boot Camp and hold the PCAP™ – Certified Associate Python Programmer (PCAP-31-03) and PCEP™ – Certified Entry-Level Python Programmer (PCEP-30-02) credentials from the Python Institute. JT has also completed AWS Educate’s Machine Learning Foundations, Deep Learning A-Z™: Neural Networks & AI, and Direct Mail Marketing coursework through LinkedIn Learning.

Marco Torresarpi brings a strong foundation in mathematics, computer science, and applied machine learning to Dark Sky Data. His work focuses on developing predictive models, statistical analysis frameworks, and AI-driven systems that transform complex datasets into structured, decision-ready insight.

Marco has developed neural network models for stock price prediction and contributed to integrating advanced artificial intelligence capabilities into financial technology platforms. His technical expertise spans Python, C++, Java, JavaScript, and OCaml, with experience implementing analytical models in production environments.

At Cornell University, Marco completed advanced coursework in artificial intelligence, machine learning, and computer vision, including 3D spatial modeling and algorithm design. His academic work emphasized pattern recognition, model evaluation, and translating theoretical methods into applied analytical systems.

Marco also led the development of the puzzle game Hellfrost, architecting its algorithmic logic and movement systems. The game is fully developed and available on the App Store.

He earned a Bachelor of Arts in Mathematics and Computer Science from Cornell University, completed the Columbia University Data Analytics Boot Camp, and holds the PCAP™ – Certified Associate Python Programmer (PCAP-31-03) and PCEP™ – Certified Entry-Level Python Programmer (PCEP-30-02) credentials from the Python Institute.

Richard Chevious brings expertise in Python development, data engineering, and machine learning to Dark Sky Data. His work focuses on developing scalable analytical software, automated data pipelines, and cloud-based systems that transform complex datasets into reliable, analysis-ready information. With experience spanning software engineering, statistical analysis, and cloud infrastructure, Richard applies a practical engineering approach to solving complex data challenges.

Richard has designed and deployed data pipelines, machine learning solutions, and web applications that support complex analytical workflows. He has also implemented secure cloud deployments, database integrations, and containerized applications designed for reliability, scalability, and performance.

At Dark Sky Data, Richard applies this technical background to the development of analytical software and data engineering solutions that support the company's analytical platform. His work helps automate data processing, strengthen analytical workflows, and transform complex datasets into actionable business intelligence.

Richard earned undergraduate degrees in Physics and Psychology, with minors in English and Mathematics. His academic work included rebuilding an observatory with an automated dome and telescope mount for variable star tracking, while his master's research focused on eclipsing binary light-curve modeling. This combination of scientific research and engineering experience provides a strong foundation for his work in software engineering, data engineering, and machine learning.

Thanos Drossos brings expertise in applied artificial intelligence, data science, and information systems engineering to Dark Sky Data. His work combines machine learning, software development, and business informatics to build analytical systems that transform complex, unstructured data into reliable, decision-ready insight. His background spans academic research and applied software development, with experience designing analytical tools that bridge advanced AI techniques and real-world business applications.

Alongside his graduate studies, Thanos conducts research on the behavior and reliability of large language models. At the Karlsruhe Institute of Technology, he has completed advanced coursework in innovation management, artificial intelligence, blockchain, and quantitative finance. He is also the lead author of a peer-reviewed study on automated market makers and liquidity provisioning strategies in decentralized exchanges, published in the proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC '25). His work reflects a broader interest in building analytical systems that are both technically rigorous and operationally reliable.

Beyond research, Thanos has developed production-oriented AI applications, including conversational large language model systems, natural language processing pipelines, and automated data extraction tools that convert unstructured information into analysis-ready datasets. He has also built compliance automation solutions supporting the European Union's Digital Operational Resilience Act (DORA). His consulting experience with zeb and d-fine spans financial regulation, blockchain, and digital assets, providing experience at the intersection of technology, analytics, and financial services. His technical expertise includes Python, Java, R, C++, SQL, transformer-based natural language processing, large language model APIs, and cloud data platforms such as Snowflake.

At Dark Sky Data, Thanos applies this background to the development of AI-driven analytical products, data pipelines, and software tools that help clients transform portfolio and operational data into structured insight. His work supports the continued expansion of Dark Sky Data's analytical capabilities across warranty, automotive finance, and other data-intensive industries.

Thanos is completing a Master of Science in Information Systems at the Karlsruhe Institute of Technology, where he maintains the highest grade on the German academic scale. He earned a Bachelor of Science in Industrial Engineering from KIT and was awarded the Deutschlandstipendium academic scholarship. He has studied and conducted research across five countries, including research appointments at the Universidad Nacional Autónoma de México (UNAM) and the Universidad Politécnica de Madrid, and is fluent in German, Greek, English, and Spanish.

Gina understands the importance of data analytics in fueling a company’s growth. With decades of experience working with data-driven companies, Gina Cocking brings a wealth of expertise in managing complex business transactions and growth strategies. She recognizes data's critical role in scaling a company and mid-sized companies' challenges in managing and optimizing their data. As the CEO and Managing Director of Colonnade Advisors, she has overseen numerous mergers and acquisitions for mid-sized companies across diverse industries.

Over her career, Gina has led billions of dollars of M&A transactions while at Colonnade Advisors, J.P. Morgan, Madison Dearborn Partners, and Kidder Peabody.

Gina not only has advisory experience, but also operational. She was the Chief Financial Officer and Chief Human Resources Officer at Cobalt Finance, where she led the company’s financial strategy, and at Healthcare Laundry Systems, a private-equity-backed company, where she successfully orchestrated its sale to a strategic acquirer. Additionally, she served as Line of Business CFO for Consumer Banking and Lending at Discover Financial Services.

Gina holds an AB in Economics and an MBA from the University of Chicago, and she currently serves on the Board of Directors of CIB Marine Bancshares, Inc. She also holds Series 24, 28, 79, and 99 securities licenses.

Dark Sky Data Wants You to Win With Data Analytics

Data is critical to your operations. It’s the underpinning to everything you do - the input to your outputs.

Our Crew

JT Turner

Marco Torresarpi

Richard Chevious

Thanos Drossos

Gina Cocking

July 2024 New Car Sales