We are seeking candidates with a strong quantitative background and hands-on programming experience. A Master’s degree in a quantitative field (e.g., Mathematics, Computer Science, Statistics, Physics) or equivalent professional experience is preferred. Candidates should have at least 2+ years in a quantitative, data science, or algorithmic role and demonstrate strong problem-solving abilities with the capacity to explain complex concepts to non-technical stakeholders. Proficiency in Python is essential, with the ability to move between research notebooks (e.g., Jupyter) and production-grade code. Solid SQL skills (including joins, aggregations, window functions, and case logic) and experience working with complex JSON data are required. Comfortable working in a Linux-based environment, including Docker, and familiarity with AWS services (EC2, SageMaker, AWS CLI, S3 workflows) are highly desirable. A natural curiosity about markets and a track record of building trading strategies, backtests, simulations, or optimization models—whether professionally or personally—will help you excel in this role. You should be a creative thinker who thrives on collaboration with engineers and analysts to deliver measurable impact on P&L and risk controls.
InTune Analytics is building a collaborative, growth-oriented environment focused on advancing quantitative research and production-grade trading systems. The role sits at the intersection of quantitative research, data science, and systems engineering, with a strong emphasis on turning research into scalable, capital-deployable strategies. You will work with large, noisy datasets from exchanges and inventory systems to identify pricing inefficiencies and translate them into systematic trading and capital allocation models. Collaboration with leadership, engineers, and analysts will be essential to continuously optimize capital deployment across our investment portfolio. This position is ideal for a creative, technically strong candidate who has built trading strategies, backtests, simulations, or optimization models—whether in a professional setting or personal projects.
- Analyze large, complex datasets using statistical methods and machine learning to identify arbitrage opportunities and market inefficiencies.
- Design and execute backtests and simulations to evaluate systematic trading strategies under real-world constraints.
- Develop models, controls, and tooling to manage risk, drawdowns, and large loss events.
- Improve and optimize capital allocation and deployment strategies.
- Transition research ideas from hypotheses to live production systems with ongoing performance monitoring.
- Collaborate with engineers to productionize research and models into live trading systems.
- Enhance shared research frameworks, tooling, and data infrastructure for efficiency and scalability.
Experience designing and deploying production-scale trading systems, strong communication skills for cross-functional teams, familiarity with risk management frameworks and framework-level testing, and a demonstrated ability to translate theoretical research into practical, scalable investment tools. Prior exposure to arbitrage strategies, market microstructure, or order-book dynamics is a plus. Self-motivation, strong attention to detail, and the ability to operate effectively in a fast-paced, risk-aware environment are highly valued.
