Head of Applied AI & Trading Systems (Iron Man Track)

Deeter Analytics is a privately held investment research and trading firm managing approximately $500M across public markets, alternatives, and private investments.

We operate with a small, senior, high-performance team in a deeply entrepreneurial culture. We value autonomy, clarity, technical rigor, and speed of execution. We hire very few people and expect each to operate at founder-level ownership.

This role sits at the center of our trading advantage.

We are hiring a Head of Applied AI & Trading Systems to build and own the technical layer that powers our discretionary investment process.

This is not a quant role.
This is not a reporting role.
This is not a “data science” role.

This is a Human-in-the-Loop AI role: building an Iron Man suit for the trader — a system that compresses information, surfaces signal, and accelerates decision-making without replacing human judgment.

Your mandate is simple and brutal:

Reduce time-to-insight across the entire trading operation.

You will design and deploy systems that ingest massive streams of unstructured data, filter noise, extract meaning, and push high-confidence intelligence directly into the hands of the CIO and trading team — in real time.

This role is ideal for a technical founder-type who loves markets, hates latency, and wants to build tools that actually get used every day.

1. Information Pipelines
You will build the nervous system of the firm.

• Design and maintain automated pipelines that ingest:

• News, filings, earnings calls, macro releases

• Social and sentiment data

• Alternative and proprietary datasets

• Replace manual “F5” workflows with push-based alerts that surface only what matters

• Maintain high-reliability API integrations with trading, research, and data platforms

2. AI-Augmented Research
You will turn AI into a live analyst sitting on the trading desk.

• Deploy LLMs to summarize, extract, and compare:

• 10-Ks, 10-Qs, earnings calls, central bank minutes
  Sell-side research and internal notes

• Build “chat with our data” interfaces so traders can query proprietary research in natural language
  Create tools for fast discretionary back-testing:

• “How did this asset behave during the last three macro shocks of this type?”

3. Signal & Noise Filtering
You will build the filters that protect attention.

• Sentiment and relevance scoring for news and social data

• Entity recognition that maps breaking events to:

  • Watchlists

  • Live positions

  • Risk exposure

• Dashboards that surface regimes, anomalies, and dislocations — not vanity metrics

4. Infrastructure, Security & Reliability
You will own the compute stack that runs the intelligence layer.

• Manage cloud and/or local GPU infrastructure for inference and data processing

• Implement data-privacy-first architectures (local models where required)

• Ensure the firm’s proprietary data and strategies never leak

Technical Foundation

• Python (Pandas, NumPy) — non-negotiable

• Experience with:

  • Vector databases (Pinecone, Milvus, FAISS, etc.)

  • Orchestration / pipelines (LangChain, Airflow, custom agents)

  • REST & WebSocket APIs (financial data, social feeds, internal tools)

• Hands-on experience with:

  • LLMs

  • RAG architectures

  • Prompting and model evaluation

• Ability to build lightweight internal tools (Streamlit, Dash, Retool, etc.)

You don’t need to be a trader — but you must be comfortable reasoning from data related to markets and companies, including:

• Market data (prices, volume, volatility, curves, spreads, positioning, regimes)

• Company data (financials, fundamentals, disclosures)

• Company media and event-driven information, evaluated critically rather than narratively

You have worked in or around high-autonomy, high-stakes environments (e.g., prop trading, family office, startup, or similar), and are comfortable making judgment calls under uncertainty.

We are optimizing for people who have:

• Pragmatism over theory
  You build things that work by market open — not in six months.

• Discretionary empathy
  You understand that the trader’s intuition is the final decision engine.
  Your systems exist to amplify, not replace it.

• Founder-level ownership
  You treat this system as if your P&L depends on it — because it does.