DRW·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

DRW ML Engineer interview with a meaty quantitative coding problem centered on portfolio optimization. The whole thing felt more like a quant research exercise than a standard ML screen, which I wasn't fully expecting.

Questions Asked (1)

Given a pandas DataFrame of daily asset returns and an annualized risk-free rate, implement a simulation-based portfolio optimizer in Python that generates N random long-only portfolios, computes annualized return, volatility, and Sharpe ratio for each, handles missing values and uneven asset histories, identifies the max-Sharpe portfolio, and returns all simulation results sorted by Sharpe. Use NumPy/pandas vectorization where possible and include docstrings with complexity notes.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This was the whole interview basically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data assumptions and handling missing values with forward-fill or dropna, then implement a vectorized Monte Carlo simulation using NumPy to generate random long-only weights. Compute annualized return, volatility, and Sharpe ratio for each portfolio, identify the max-Sharpe one, and return results sorted by Sharpe, ensuring code is clean with docstrings and complexity notes.

Pro tip: Emphasize the importance of aligning asset histories and handling missing data robustly, as real-world financial data is messy; also mention that vectorization is key for performance in simulation-heavy tasks.

1. Clarify data and assumptions

Confirm the DataFrame structure (dates as index, assets as columns), the risk-free rate, and how to handle missing values and uneven histories (e.g., forward-fill, drop, or mask).

2. Preprocess returns

Clean the data by handling NaNs and ensuring all assets have sufficient overlapping history; compute daily returns if needed and align dates.

3. Generate random portfolios

Use NumPy to generate N random weight vectors that sum to 1 and are non-negative (long-only), leveraging vectorization for efficiency.

4. Compute metrics

For each portfolio, calculate annualized return, volatility, and Sharpe ratio using vectorized operations on the returns matrix and weights.

5. Identify max-Sharpe and sort results

Find the portfolio with the highest Sharpe ratio, and return a DataFrame of all simulations sorted by Sharpe ratio descending.

Key Points to Mention

Handling missing values and uneven asset histories (e.g., forward-fill, dropna, or using a mask)
Vectorization with NumPy for generating random weights and computing portfolio metrics
Annualization of return and volatility (e.g., multiply mean daily return by 252, volatility by sqrt(252))
Sharpe ratio calculation: (portfolio return - risk-free rate) / portfolio volatility
Long-only constraint: weights sum to 1 and are non-negative
Complexity analysis: O(N * T * A) for N simulations, T time periods, A assets, and how vectorization reduces constant factors

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.