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.

Key Responsibilities:

Research and hypothesis generation:

• Translate investment ideas into testable hypotheses with clear metrics and failure criteria
• Build 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 availability
• Implement 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 comparisons
• Perform robustness analysis: turnover, drawdowns, concentration, regime sensitivity, stability across time and cohorts
• Backtesting and portfolio construction
• Implement or extend low-frequency backtests for signals and portfolios
• Model 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 next
• Present 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)
• Experience We Value (Any Combination)
• 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