Senior Data Scientist, Applied ML

• Work side by side with traders and analysts to turn market questions into scoped projects, each with a clear commercial hypothesis and a defined go/no-go point.
• Review progress with the desk throughout each project, not just at handoff. Show intermediate results early, test them against the desk’s market knowledge, and change course when the signal or the priority shifts.
• Build LLM, NLP and multimodal pipelines that extract structured signals from unstructured sources such as outage reports, news, filings, and web content.
• Design and run quick experiments to test feasibility across modelling approaches, and stop weak ideas early.
• Build models that predict outage impacts, classify disruption patterns, and detect anomalies and regime changes in fundamental data.
• Take the models that prove out into production. That means packaged, tested, deployed through CI/CD, with monitoring and drift detection built in from day one.
• Deliver insights where traders already work: Teams, alerts, and the Vision Excel and Python tooling. Standalone dashboards are not the default.
• Source data through the Vision platform. When you need a new source, work with the Vision team rather than building side pipelines, and follow the existing web-scraping architecture.
• Evaluate commercial AI platforms and tools against real desk problems, and give a clear recommendation.
• Explain results, uncertainty, and limits in plain terms to traders, analysts, and senior stakeholders.

Qualifications

• Applied experience: 5+ years applying machine learning and NLP to messy, real-world data, including models you took to production and supported after launch.
• Desk-facing track record: You have worked directly with commercial users in trading, finance, or a similar fast-paced setting, and you can point to decisions your work changed.
• Quantitative foundation: Master’s or PhD in statistics, mathematics, physics, engineering, or equivalent depth gained in industry. Strong statistics and experimental design.
• LLMs and NLP: Hands-on experience building extraction and classification pipelines with LLMs, covering evaluation, prompt and retrieval design, and cost and latency trade-offs.
• Python and ML stack: Python with scikit-learn, PyTorch, and HuggingFace. You write production-quality code that others can review and maintain.
• Production engineering: Docker, AWS, CI/CD, and MLOps practice, including model versioning, monitoring, and retraining.
• Time series and signals: Experience with anomaly detection and with combining signals across multiple time-series and text sources.
• Communication: You can explain what a model means for a trade in two sentences, and you are comfortable being challenged by people who know the market better than you do.
• Nice to have: Knowledge of energy or commodities markets, graph modelling, sentiment analysis, or agentic workflows and MCP.

• Medical

• Medical
• Dental
• Vision
• Paid Vacation
• 401k with company contributions
• Life insurance
• Short-term & Long-term disability