Head of Applied AI – Trading Systems

🕒 February 26

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🤖 Artificial Intelligence

👻 Ghost score 69%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Deeter Analytics

WebsiteLinkedInAll Job Openings

11 - 50 employees

💸 Finance

💳 Fintech

🤝 B2B

Finance • Fintech • B2B

Deeter Analytics is a financial analytics firm that turns complex market data into actionable trading insights for investors and institutions. The company combines quantitative research, advanced analytics, and proprietary technology to help clients identify opportunities, manage risk, and improve trading performance through clear, data-driven recommendations and tools.

📋 Description

• You will design and maintain automated pipelines that ingest, clean, and normalize: • News, filings, earnings calls, macro releases • Social and sentiment data • Alternative and proprietary datasets • Your job is to replace manual refresh workflows with push-based alerts that surface only what matters — mapped directly to: • Watchlists • Live positions • Risk exposure • Reliability matters. Latency matters. Silence matters. • You will deploy LLM-powered systems to summarize, extract, compare, and reason over: • 10-Ks, 10-Qs, earnings calls, central bank minutes • Sell-side research and internal notes • You will build “chat with our data” tools that allow traders to query proprietary research in natural language. • You will also create tools for fast discretionary back-testing: • How did this asset behave during the last three macro shocks of this type? • You will build the filters that decide what breaks through. • Sentiment and relevance scoring • Entity recognition that maps events to exposure • Dashboards that surface regimes, anomalies, and dislocations — not vanity metrics • If something matters, it should scream. If it doesn’t, it should disappear. • You own the compute layer that runs the intelligence system. • Cloud and/or local GPU infrastructure • Vector databases and retrieval systems • Data-privacy-first architectures (local models where required) • You choose the architecture. You ship what runs fastest and breaks least. Our proprietary data and strategies do not leak. Ever.

🎯 Requirements

• Built your own projects • Traded your own account • Worked in a high-stakes startup, prop shop, or family office • You hate waiting for permission • Python (Pandas, NumPy) is non-negotiable • You have hands-on experience with: • LLMs and RAG architectures • Prompting, evaluation, and failure modes • Vector databases (Pinecone, Milvus, FAISS, etc.) • Orchestration (LangChain, Airflow, or custom agents) • REST & WebSocket APIs (market data, news, internal tools) • Lightweight internal UIs (Streamlit, Dash, Retool, etc.) • You know the difference between an LLM demo and a production system • You care about latency, hallucinations, and context windows • Financial data is messy, adversarial, and time-sensitive • Narratives are not facts • A 10-K is not a blog post • You are comfortable making judgment calls under uncertainty.

🏖️ Benefits

• Zero Latency • Real Impact • Sovereignty