About Deeter Investments

Deeter Investments is a founder‑led proprietary trading firm built around real‑time, data‑driven decision‑making. We prize curiosity, collaboration, and a bias for action. After years of discretionary success, we’re launching a dedicated algorithmic division—and we’re looking for a Head of Quant Trading to architect and scale this effort from day one.

Role Summary

You will spearhead the development, optimization, and deployment of cutting‑edge algorithmic strategies and quantitative models. The position blends deep hands‑on technical work with high‑level strategic oversight across research, engineering, and trading operations.

Key Responsibilities

Quantitative Strategy Development & Research

• Algorithm Design: Lead the creation and refinement of proprietary trading algorithms rooted in the firm’s market framework, leveraging advanced statistical and machine‑learning techniques.
• Modeling & Simulation: Build forecasting, signal‑generation, and risk models; run rigorous back‑tests and simulations to validate performance.
• Data Analysis: Mine large, heterogeneous datasets (market microstructure, alternative data, etc.) for actionable insights.
• Innovation: Continuously evaluate emerging research (deep learning, reinforcement learning, agent‑based modeling) to sharpen our edge.

Technical Infrastructure & Implementation

• System Architecture: Partner with engineering to design high‑throughput trading systems that scale globally.
• Software Development: Oversee codebases in Python, C++, Java, or MATLAB; enforce best practices for testing, CI/CD, and performance monitoring.
• Automation & Integration: Build end‑to‑end pipelines for data ingestion, model training, and live deployment; ensure seamless connection to execution venues and data feeds.
• Tech‑Stack Stewardship: Select and integrate best‑in‑class analytics platforms, databases, and cloud resources.

Performance Analysis & Risk Management

• Metrics & Analytics: Define and track KPIs—alpha decay, slippage, Sharpe, drawdown, and latency—via real‑time dashboards.
• Risk Controls: Embed robust risk models and dynamic hedging; enforce firm‑wide limits and compliance requirements.
• Optimization: Iterate relentlessly—parameter sweeps, sensitivity analyses, and scenario tests to future‑proof strategies.

Collaboration & Leadership

• Team Mentorship: Grow and mentor a multidisciplinary team of quants, data scientists, and engineers; cultivate a culture of experimentation and peer review.
• Documentation & Code Quality: Champion readable, well‑tested, version‑controlled code and transparent research notebooks.

Qualifications

• Education: B.S. or M.S. in a quantitative field such as Mathematics, Computer Science, Engineering, Statistics, or Physics.
• Experience: Minimum 5 years building and deploying profitable algorithmic strategies at a hedge fund, bank, or proprietary trading firm.
• Programming: Advanced expertise in at least one core language (Python, C++, or Java) and familiarity with Linux, Git, and CI workflows.
• Data Science: Deep knowledge of statistical modeling, feature engineering, and machine‑learning frameworks (PyTorch, TensorFlow, scikit‑learn).
• Systems: Proven skill in real‑time data pipelines, distributed/cloud computing, and performance optimization.
• Markets: Strong grasp of market microstructure, electronic trading protocols, and order‑book dynamics.
• Language: Fluent English (written and spoken) is required.
• Soft Skills: Exceptional analytical rigor, clear communication, and the leadership mindset to build a high‑performance team from scratch.