Machine Learning Engineer

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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

• Build machine-learning models for investment research and trading applications • Turn research ideas and raw data into models, experiments, and actionable results • Own the end-to-end loop from data and model development through infrastructure, evaluation, and write-up • Build, train, ablate, and improve deep-learning models on market data • Set up and operate a small GPU environment using a local machine or AWS instances • Manage environments, drivers, containers, storage, experiment tracking, monitoring, and compute costs • Perform leakage-proof validation on time-ordered data and regime-aware testing • Establish honest baselines and distinguish genuine results from noise • Read, reproduce, and evaluate recent research in foundation models, time series, and reinforcement learning • Write concise findings explaining experiments, results, meaning, and next steps • Use modern AI tools to accelerate coding, literature review, and data wrangling while verifying their outputs • Work directly with traders and researchers whose decisions are informed by the models • Grow from owning one project end to end to taking on more of the research agenda

🎯 Requirements

• Early-career machine learning profile with demonstrated fundamentals and building ability • No trading experience required • Strong understanding of optimization, initialization, normalization, attention, model divergence and plateaus • Knowledge of linear algebra, probability, and statistics • Ability to derive loss-function gradients and reason about batch-size changes • Evidence of independently built projects, hackathon builds, trained models, or used repositories • Strong, idiomatic Python and PyTorch skills • NumPy and pandas experience • Ability to set up and run small-scale GPU infrastructure locally or on AWS • Knowledge of CUDA, containers, storage, monitoring, and cost control • Experience with time-ordered data, leakage-free splits, backtest hygiene, and distribution shift • Ability to reproduce research papers and evaluate where methods break on the candidate's data • Fluency with modern AI tools for code, literature, and data work • Strong written communication and ability to produce clear research write-ups • Ability to work self-directed in a remote, low-guardrail environment • Fine-tuning or serving LLMs, CUDA or Triton, and time-series forecasting are bonus skills, not required

🏖️ Benefits

• Bonus • Direct work with traders and researchers in a live trading operation • Well-capitalized firm with a distinctive approach to markets • Deliberate growth path with increasing research ownership • Fully remote team