Head of Algorithmic Trading

Head of Algorithmic Trading

About the role

Deeter Analytics is a privately held investment research and trading firm managing its own capital across public markets. After years of discretionary success, we believe we have developed some distinctive ways of understanding markets. We are now building a dedicated algorithmic effort to turn those insights — and new ones we discover — into systematic, AI-driven alpha.

We are hiring a Head of Algo to lead that effort end to end. Reporting to and working directly with the Principal and a world-class team, you will turn alpha ideas into research programs, determine what actually works, and build successful ideas into a live, continuously improving mid-frequency trading operation.

A major part of the role is taking market ideas and hypotheses from the Principal — often expressed initially as observations, intuitions, or loosely defined theories — and figuring out how to test them rigorously. You will translate those ideas into measurable signals, identify the right data, design experiments, reject what doesn't work, refine what does, and ultimately turn the strongest findings into production trading strategies.

Prior stock-market or professional trading experience is not required. We are deliberately open to exceptional people from AI, machine learning, statistics, physics, engineering, forecasting, gaming, experimentation, or other quantitative fields. We care much more about whether you can reason from first principles, extract signal from noisy systems, run rigorous experiments, and build things that work in the real world.

This is a hands-on leadership role at the intersection of research, AI, experimentation, and live trading. The emphasis is on AI-native alpha generation and rapid learning—not on building ultra-low-latency infrastructure.

What you’ll own

• Turning alpha ideas into evidence. Work directly with the Principal to take discretionary market observations, theories, and alpha ideas and translate them into falsifiable hypotheses, research questions, datasets, signals, and experiments. Your job is not simply to implement ideas as given, but to understand the underlying intuition, test it intelligently, improve it where possible, and determine whether a real edge exists.

• Strategy, end to end. Take successful ideas from hypothesis generation through signal research, statistical validation, realistic backtesting, portfolio construction, live deployment, P&L attribution, and continuous iteration.

• Independent alpha discovery. Develop new signals and strategies yourself using both conventional quantitative methods and newer AI-driven approaches.

• The research-to-production pipeline. Own the complete path from an interesting observation to a live position: data, signal generation, validation, sizing, portfolio construction, execution logic, deployment, monitoring, and iteration.

• AI-native research. Develop proprietary models where that creates an advantage and leverage existing frontier AI models where they are the better tool. Build workflows that allow the team to investigate far more hypotheses than would traditionally be possible.

• Team and collaboration. Build a small, unusually strong team as the effort scales and work closely with the Principal, discretionary researchers, engineers, and data team. This seat is not siloed.

Who you are

We hire for demonstrated ability and how you think, not pedigree or industry background.

You do not need prior experience trading stocks or working at a hedge fund. In some cases, we may prefer an exceptional researcher or builder coming to markets with fresh eyes over someone trained to approach problems the same way everyone else in the industry does.

Your background might be in machine learning, AI research, statistics, physics, mathematics, computer science, forecasting, optimization, gaming, experimentation, or another field involving difficult decisions under uncertainty. It might come from academia, industry, competitions, independent projects, or a self-taught path.

What matters is evidence that you are:

• A strong first-principles thinker with real quantitative and technical depth.
• Exceptionally good at turning vague ideas into concrete, testable questions.
• Able to understand the intuition behind someone else's hypothesis rather than mechanically implementing a specification.
• Rigorous about evidence and willing to kill an attractive idea when the data says it doesn't work.
• Comfortable operating in noisy environments where there is no clean ground truth.
• Candid about uncertainty and quick to revise your view when evidence changes.
• Low ego and resilient: open to challenge and comfortable being wrong in pursuit of the right answer.
• Able to take a broad mandate, determine what actually needs to be done, and deliver.
• Curious and genuinely energized by difficult, unsolved problems.

How you work

• AI-native. You are fluent in modern AI and use it as leverage across research: exploring hypotheses, extracting information from unstructured data, writing and analyzing code, designing experiments, building models, and accelerating iteration. You need not be the firm's deepest AI researcher, but you should understand the technology well enough to direct sophisticated work.

• Empirical and iterative. You establish baselines, run cheap experiments before expensive ones, and progressively increase rigor as evidence accumulates. You understand that most hypotheses will fail and design the research process accordingly.

• First-principles. You don't assume the conventional way of approaching a market problem is the correct one. You try to understand the underlying mechanism and determine what information would actually prove or disprove it.

• Pragmatic. You select the right method for the problem. A simple statistical model that works is preferable to an impressive model that doesn't. You optimize for learning and ultimately P&L, not sophistication for its own sake.

• Clear and collaborative. You can have a productive conversation with someone who has strong market intuition but may describe an idea differently from how a quantitative researcher would formalize it. You ask the right questions, communicate findings clearly, and challenge assumptions without unnecessary friction.

Core skills

• Quantitative modeling. Strong facility with statistics, machine learning, forecasting, optimization, or related quantitative methods. You understand how to determine whether apparent predictive power is likely to be real.

• Experiment design and data sense. You know how to take an ambiguous hypothesis, identify the relevant variables, establish a baseline, design a useful experiment, and distinguish genuine evidence from noise.

• Messy-data reasoning. You are comfortable extracting useful information from imperfect datasets, including alternative and unstructured data such as text, social media, behavioral data, and event data.

• Signal-versus-noise judgment. You understand multiple testing, leakage, overfitting, regime dependence, selection bias, and the many ways a compelling result can be false. You combine statistical discipline with judgment rather than hiding behind a single metric.

• Modern AI. You understand where frontier models, embeddings, deep learning, reinforcement learning, agents, and related techniques can create genuine leverage—and where simpler methods are more appropriate.

• Strong hands-on coding. You can rapidly turn an idea into an experiment and an experiment into a robust system. Python and the modern data/ML ecosystem should be second nature.

• Systems thinking. You can eventually own the path from research into live trading, including data pipelines, portfolio construction, execution, deployment, monitoring, and sensible buy-versus-build decisions. We do not require you to arrive knowing every detail of trading infrastructure or market microstructure; we do require you to be capable of learning it quickly.

What we offer

• Direct partnership with the Principal and substantial ownership of a new, high-impact effort.
• A large backlog of proprietary market observations and alpha hypotheses waiting to be rigorously investigated.
• The freedom to approach markets from first principles rather than inherit a conventional quant playbook.
• A well-capitalized firm with a distinctive, research-led approach to markets.
• The ability to build the team and research process around what actually works.
• Significant upside tied to performance.
• Compensation: $250k–$500k base + upside exposure.