• Identify predictive patterns in alternative textual and tabular data sources • Mine time‑series and cross‑sectional relationships in high‑frequency and daily tabular data • Build and compare NLP architectures (transformers, embeddings, topic & sentiment models) • Develop statistical and machine‑learning models (linear factor, tree‑based, gradient boosting, neural nets) that combine text‑derived features with numeric factors • Construct robust, transaction‑cost‑aware back‑tests • Partner with Data Engineering to scale data pipelines and feature stores • Work with Portfolio Engineering to integrate signals into systematic strategies and monitor live performance • Present findings to senior leadership; contribute to Stormlight’s research culture through white‑papers, internal talks, and code reviews