Role summary
Tirium is looking for a Quantitative Research Analyst to identify, investigate, and systematically exploit market inefficiencies across public markets.
You will be working at the intersection of quantitative finance, statistics, alternative data, and technology, turning market observations into testable hypotheses, signals, forecasts, and live investment decisions. The role spans the full research lifecycle, from exploratory analysis and model development to backtesting, portfolio integration, production monitoring, and post-trade learning.
Your work will directly influence Tirium's investment models, portfolio construction, and allocation of proprietary capital.
There are no external clients and no requirement to produce research for marketing purposes. The objective of research is straightforward: to improve the quality of the firm's investment decisions.
About Tirium
Tirium Capital is a proprietary trading firm built around a scientific approach to markets.
At the center of our investment process is a proprietary research-to-production platform that integrates data, research, simulation, forecasting, portfolio construction, execution, and monitoring into a continuous feedback loop.
The Firm has remained profitable in every year over the past decade, navigating periods of expansion, contraction, elevated volatility, and significant market stress.
Core responsibilities
Strategic goals
The goal is to discover statistically significant patterns and determine whether they represent durable and investable opportunities after costs, liquidity constraints, market impact, and risk.
Typical objectives include:
- Identify potential sources of alpha across equities, derivatives, macro instruments, and other liquid public markets.
- Research structural, behavioral, fundamental, statistical, and information-driven market inefficiencies.
- Convert investment hypotheses into measurable signals and predictive models.
- Improve the firm's understanding of market regimes, risk factors, cross-asset relationships, and changing market structure.
- Evaluate strategies for robustness, capacity, scalability, decay, and economic relevance.
- Contribute to new strategies, portfolio sleeves, overlays, and risk-management mechanisms.
Research and execution
You will own or contribute to research projects from initial hypothesis through live deployment.
Typical responsibilities include:
- Analyze market, fundamental, macroeconomic, alternative, and proprietary datasets.
- Develop factors, signals, features, forecasting models, and systematic trading rules.
- Apply statistics, econometrics, time-series analysis, optimization, and machine learning where appropriate.
- Design rigorous experiments and historical simulations.
- Evaluate models through out-of-sample, walk-forward, sensitivity, stress, and regime analysis.
- Measure turnover, liquidity, transaction costs, market impact, leverage, concentration, drawdowns, and tail behavior.
- Detect overfitting, data leakage, survivorship bias, look-ahead bias, and other research failures.
- Monitor live signal and strategy performance and investigate model degradation or alpha decay.
- Analyze differences between simulated and realized performance.
- Produce clear research documentation covering hypotheses, methodology, evidence, assumptions, and limitations.
Collaboration
Portfolio and investment collaboration
You will work closely with portfolio managers, researchers, traders, and risk specialists to translate research into portfolio decisions.
Responsibilities may include:
- Analyze dependencies between signals, factors, strategies, and positions.
- Support portfolio construction, capital allocation, and position sizing.
- Evaluate diversification benefits and hidden factor exposures.
- Study portfolio behavior across different market regimes.
- Analyze the relationship between expected return, volatility, liquidity, capacity, and drawdown.
- Contribute to hedging and risk-control methodologies.
- Conduct portfolio and performance attribution.
A strong Quantitative Research Analyst should understand not only whether a model works, but how it changes the overall portfolio's behavior.
Technology and data collaboration
Researchers at Tirium work closely with quantitative developers and data engineers rather than handing research over to a separate implementation organization.
You will:
- Translate research prototypes into reliable production models.
- Define data requirements and evaluate new datasets.
- Validate production implementations against research models.
- Improve research tooling, automation, and reproducibility.
- Help design monitoring for signals, strategies, models, and data quality.
- Identify opportunities to improve the firm's research-to-production platform.
Firm development contribution
You will also contribute to the intellectual and organizational development of the quantitative research function.
Depending on seniority, this may include:
- Presenting and defending research findings.
- Reviewing and challenging the work of other researchers.
- Participating in research discussions across markets, economics, statistics, and portfolio construction.
- Mentoring junior researchers.
- Helping define research standards and review processes.
- Participating in quantitative hiring and candidate assessment.
- Contributing to the structure and evolution of the research team.
Qualifications
We care more about analytical depth, intellectual rigor, and demonstrated research ability than about checking every possible credential.
Must-have qualifications
Hard skills
A strong academic background in mathematics, statistics, physics, computer science, econometrics, quantitative finance, engineering, or another highly quantitative discipline is expected.
A master's degree, PhD, or equivalent level of training is preferred, although exceptional candidates with strong demonstrated research ability will also be considered.
We look for:
- Strong foundation in probability, statistics, and quantitative methods.
- Knowledge of statistical inference, hypothesis testing, time-series analysis, and experimental design.
- Strong Python skills and working proficiency with SQL.
- Experience developing and evaluating quantitative models.
- Strong understanding of backtesting and common research biases.
- Ability to analyze large, noisy datasets and distinguish genuine relationships from statistical artifacts.
- Understanding of investment concepts such as returns, volatility, correlation, beta, factor exposure, drawdown, liquidity, turnover, and transaction costs.
- Ability to communicate quantitative findings clearly.
Experience
For the standard-level position, we typically expect approximately 2-5 years of relevant experience in environments such as:
- Proprietary trading firms
- Hedge funds
- Quantitative asset managers
- Systematic trading teams
- Investment-bank quantitative research groups
- Market-making firms
Candidates from adjacent scientific or machine-learning research environments may also be considered if they demonstrate strong understanding of markets and investment research.
Soft skills
We look for:
- Scientific and hypothesis-driven thinking.
- Strong critical reasoning and problem decomposition.
- Intellectual curiosity and interest in financial markets.
- Comfort reasoning under uncertainty.
- Research discipline and attention to methodological rigor.
- Pragmatism about model complexity.
- Ownership of open-ended problems.
- Intellectual independence combined with willingness to change conclusions when evidence changes.
Nice-to-have qualifications
Hard skills
Experience in one or more of the following areas is valuable:
- Econometrics and Bayesian statistics
- Machine learning and deep learning
- Optimization and stochastic processes
- Causal inference
- Alternative-data research
- Factor modeling and statistical arbitrage
- Systematic macro
- Volatility and derivatives
- Market microstructure
- Portfolio optimization and risk modeling
- Regime detection
- NLP and unstructured financial data
Experience with tools such as NumPy, pandas, Polars, SciPy, scikit-learn, statsmodels, PyTorch, JAX, Spark, or similar environments is beneficial.
Experience
Particularly relevant experience includes:
- Developing signals or strategies deployed with real capital.
- Alpha research across equities or other liquid markets.
- Working with alternative datasets.
- Building systematic long/short strategies.
- Portfolio construction or capital-allocation research.
- Transaction-cost and capacity analysis.
- Regime-aware investment modeling.
- Strategy decay analysis.
- Close collaboration with portfolio managers or traders.
Serious personal research, trading, academic publications, or open-source quantitative work can also strengthen an application.
What success looks like
Over time, a successful Quantitative Research Analyst will:
- Generate original investment hypotheses and develop them into robust models.
- Produce research that survives out-of-sample testing and real-world trading constraints.
- Improve existing models and detect when previously profitable relationships begin to weaken.
- Contribute to better portfolio construction and risk-adjusted performance.
- Increase the speed and quality of Tirium's research-to-production cycle.
- Build reusable knowledge that compounds across future research projects.
Ultimately, success is measured not by the number of models produced, but by the quality of the investment knowledge created and its impact on risk-adjusted returns.
Compensation and benefits
Tirium aims to offer a package competitive with leading proprietary trading firms and quantitative investment organizations.
Compensation
- Above-market base salary.
- Significant annual performance bonus.
- Compensation linked to individual contribution, team performance, and firm results.
- Meaningful upside for researchers whose work creates durable economic value.
- Long-term incentive opportunities for exceptional performers and senior employees.
Research environment
- Direct access to portfolio managers and senior decision-makers.
- Ability to follow research from hypothesis through live deployment.
- Access to high-quality market, fundamental, macroeconomic, and alternative datasets.
- Modern quantitative research infrastructure and significant computing resources.
- Budget for promising new datasets and research tools.
- Minimal bureaucracy around testing credible investment ideas.
Professional development and benefits
- Conference, academic-event, book, and research-publication budget.
- Support for relevant professional qualifications and technical training.
- Internal research seminars and dedicated exploratory research time.
- Flexible working arrangements.
- High-end research equipment.
- Generous paid time off.
- Private health insurance.
- Wellness and fitness allowance.
- Retirement or pension contributions where applicable.
- Relocation and visa support for exceptional candidates where appropriate.
Tirium offers quantitative researchers the chance to tackle intellectually challenging problems with measurable real-world impact.
