Welcome to My Professional Portfolio

Hello! I'm Jordan Moore, Senior Quant and data research leader at Impossible Cloud Network. I build end-to-end data pipelines that feed machine learning, artificial intelligence and data-science workflows - telemetry comes in, clear product and trading decisions come out.

2024-2025 - Leading data at Impossible Cloud

• Run a four-person quant and data-engineering pod, setting research road-maps, sprints and KPIs across ingestion, modelling, training and BI.
• Authored the class-based Monte-Carlo simulator that sweeps nine demand and unlock paths and drives every MVP parameter sweep.
• Stress-tests and scenario analysis lifted collateral floors from 30 % to 50 %, now codified in the Solidity spec.
• Built a Databricks -> Delta -> Tableau chain that ingests GraphQL and CCXT feeds, engineers features, trains forecasts and renders six launch dashboards in under five minutes.
• Wired PagerDuty and Slack alerts straight from Python notebooks for economic-health and model-drift checks.
• Own investor GitBook pages, one-pagers and economic explainer decks. Mentor an intern and a junior engineer on ML ops and software craft.

Earlier career highlights

Marmalade Game Studio (2023-2024)

• Doubled user-acquisition efficiency with ML lifetime-value models and causal lift tests.
• Shipped Tableau dashboards, Prophet forecasts and reinforcement-learning price tests across global markets.

Jagex (2021-2023)

• Built churn predictors and embeddings for RuneScape player cohorts; guided membership price changes.
• Authored a recession-impact white-paper combining econometrics and AI-generated scenarios.

Epimorphics (2021) - improved public-health alert SEO with Schema.org metadata and a Python data-quality toolkit.

Academic foundation

PhD in Mathematics, University of Exeter - automated defect quantification plus teaching in statistics and numerical methods.

Core skill set

• Python, C++, SQL, Bash - simulation, data engineering, ML ops.
• AWS, Databricks, Snowflake, Airflow, Docker - cloud-scale orchestration.
• Pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, XGBoost - ML and AI.
• Tableau, Plotly, Streamlit - data apps and dashboards.
• GitBook, Jupyter, LaTeX - documentation and reproducible science.

Away from the keyboard you'll find me coaching judo or experimenting in the kitchen - hobbies that balance precision with creativity.