Quantitative Researcher
Job Summary:
Plutus21 is looking for a Quantitative Researcher to discover, test, and improve systematic alpha signals and portfolio construction for low-frequency equity strategies (typically daily to monthly horizons). This role is designed for exceptional quantitative thinkers coming from Physics, Mathematics, Statistics, Engineering, or other rigorous fields.Location: Remote
Key Responsibilities:Research and hypothesis generation:
Translate investment ideas into testable hypotheses with clear metrics and failure criteriaBuild simple baselines first, then iterate toward stronger models only when justified Data and features (research-grade)Work with panel/time-series equity data and build features with strict as-of availabilityImplement careful data checks (missingness, outliers, corporate actions, calendar alignment)
Evaluation and robustness:
Design validation protocols appropriate for time series (walk-forward, rolling windows, cross-sectional splits)Detect and prevent common research pitfalls: look-ahead bias, leakage, overfitting, multiple comparisonsPerform robustness analysis: turnover, drawdowns, concentration, regime sensitivity, stability across time and cohorts Backtesting and portfolio constructionImplement or extend low-frequency backtests for signals and portfoliosModel basic frictions realistically (transaction costs, slippage assumptions, liquidity/turnover constraints)Collaborate with engineering/trading to productionize the strongest research findings
Communication and collaboration
Write clear research memos: what you tried, what worked, what didnt, what you recommend nextPresent results transparently, including uncertainty, limitations, and risk considerations
Qualifications (Core):
Strong quantitative foundation in probability/statistics and at least one of: linear algebra, optimization, numerical methods Ability to design experiments and reason about measurement (baselines, controls, uncertainty, sanity checks) Ability to write working analysis code in Python (preferred) or another language, and communicate code/results clearly Comfort with real-world messy datasets and non-stationary behavior Strong written communication and intellectual honesty (you can say this is inconclusive and explain why) Prior research experience (academic, industry, independent) demonstrating end-to-end ownership Evidence of strong software fundamentals even without formal CS training: readable code, modularity, reproducibility Work involving time-series or observational data where leakage is a risk (forecasting, causal inference, experiments)
Nice to Have (Not Required):
Any exposure to markets, equities, factor models, or portfolio construction (we can teach this) Familiarity with common research tools: numpy/pandas/scipy/statsmodels/sklearn, Jupyter, Git Experience with simulation/Monte Carlo, Bayesian methods, or causal inference
Apply Now
Plutus21 is looking for a Quantitative Researcher to discover, test, and improve systematic alpha signals and portfolio construction for low-frequency equity strategies (typically daily to monthly horizons). This role is designed for exceptional quantitative thinkers coming from Physics, Mathematics, Statistics, Engineering, or other rigorous fields.Location: Remote
Key Responsibilities:Research and hypothesis generation:
Translate investment ideas into testable hypotheses with clear metrics and failure criteriaBuild simple baselines first, then iterate toward stronger models only when justified Data and features (research-grade)Work with panel/time-series equity data and build features with strict as-of availabilityImplement careful data checks (missingness, outliers, corporate actions, calendar alignment)
Evaluation and robustness:
Design validation protocols appropriate for time series (walk-forward, rolling windows, cross-sectional splits)Detect and prevent common research pitfalls: look-ahead bias, leakage, overfitting, multiple comparisonsPerform robustness analysis: turnover, drawdowns, concentration, regime sensitivity, stability across time and cohorts Backtesting and portfolio constructionImplement or extend low-frequency backtests for signals and portfoliosModel basic frictions realistically (transaction costs, slippage assumptions, liquidity/turnover constraints)Collaborate with engineering/trading to productionize the strongest research findings
Communication and collaboration
Write clear research memos: what you tried, what worked, what didnt, what you recommend nextPresent results transparently, including uncertainty, limitations, and risk considerations
Qualifications (Core):
Strong quantitative foundation in probability/statistics and at least one of: linear algebra, optimization, numerical methods Ability to design experiments and reason about measurement (baselines, controls, uncertainty, sanity checks) Ability to write working analysis code in Python (preferred) or another language, and communicate code/results clearly Comfort with real-world messy datasets and non-stationary behavior Strong written communication and intellectual honesty (you can say this is inconclusive and explain why) Prior research experience (academic, industry, independent) demonstrating end-to-end ownership Evidence of strong software fundamentals even without formal CS training: readable code, modularity, reproducibility Work involving time-series or observational data where leakage is a risk (forecasting, causal inference, experiments)
Nice to Have (Not Required):
Any exposure to markets, equities, factor models, or portfolio construction (we can teach this) Familiarity with common research tools: numpy/pandas/scipy/statsmodels/sklearn, Jupyter, Git Experience with simulation/Monte Carlo, Bayesian methods, or causal inference
Apply Now