Founding Quant/Trading Strategist

About Us

We are building a next-generation AI-driven trading platform(Ai-CoPilot)

You'll be the scientist who turns/tests our hypotheses/science into fundamental, risk-aware strategies.

Role Description

alpha discovery ? validation ? portfolio construction ? live monitoring

DIY mindset

Qualifications

• Strong Market Risk and Analytical Skills, Experience in Quantitative Analytics
• Ability to work independently and remotely
• Relevant experience within the financial or trading industry is a plus
• Bachelor's degree in Finance, Mathematics, Statistics, Economics, or a related field

• 8+ years

  in quantitative research/trading or an equivalent track record of shipping strategies.
• Deep skills in

  Python

  (NumPy/Pandas/Numba/Statsmodels), time-series/stat inference, and ML for markets.
• Proven experience with

  bias-free backtesting

  , cross-validation for dependent data (e.g.,

  CPCV

  ), and cost/impact modeling.
• Firm grasp of market microstructure and risk (exposures, drawdowns, regime shifts).
• Startup DNA: skeptical, fast, documentation-minded, and comfortable owning results end-to-end.

What You'll Do

• Alpha research & hypothesis testing:

  Generate/verify signals across price, volatility, fundamentals, and alt-data using rigorous statistics; separate luck from skill with robust tests and out-of-sample evidence.

• Backtesting & validation:

  Build leak-proof pipelines (no look-ahead/survivorship), use

  purged/walk-forward/CPCV

  splits, and apply metrics like

  Deflated Sharpe

  to reduce backtest overfitting.

• Portfolio construction & risk:

  Translate signals into positions with constraints, drawdown controls, and stress tests across regimes; model slippage/fees/borrowing and reality-check edge after costs.

• Execution awareness:

  Define execution/SLO assumptions (latency budgets, order types, child-order logic) and partner with engineering on safe-guards/kill-switches for live strategies.

• Model governance:

  Document assumptions, data lineage, and versioning; run champion-vs-challenger tests; practice independent validation consistent with leading

  model risk management

  principles.

First 90-120 Days (outcomes)

• Ship a leak-proof research framework with CPCV/walk-forward, cost modeling, and standardized reports.
• Produce two vetted strategies that beat defined baselines after costs; document model cards and rollback criteria aligned with governance best practices.
• Define live monitoring KPIs (edge decay, drift tests, latency/fill SLOs) and kill-switch thresholds consistent with algorithmic-trading control guidance.

Why Join

meaningful equity