Top 25 D.E. Shaw Interview Questions & Answers

1. Introduction

Embarking on the interview process with D.E. Shaw can be a formidable challenge; it is a company known for its rigorous selection criteria. Candidates often find themselves facing a diverse array of de Shaw interview questions, designed to probe deeply into their skillset, personality, and professional acumen. This article aims to shed light on some of the questions you might encounter and offer guidance on how to navigate them effectively, potentially setting you on the path to joining one of the most prestigious firms in the finance industry.

2. Navigating D.E. Shaw’s Interview Landscape

D.E. Shaw & Co. stands as a paragon of innovation and excellence in the world of investment management, often characterized by its meticulous attention to detail and quantitative approach to finance. With a reputation for hiring some of the brightest minds, D.E. Shaw ensures that each candidate is not only technically proficient but also culturally fit to contribute to their collaborative environment. A successful candidate must exhibit a deft blend of analytical prowess, creativity, and strategic thinking. The questions posed during their interviews are meticulously crafted to gauge these qualities, offering a window into how potential hires think, react, and problem-solve under varying circumstances. As we delve into the types of questions that define D.E. Shaw’s interview process, remember that preparation, clarity of thought, and authenticity remain your indispensable allies.

3. DE Shaw Interview Questions

Q1. Can you walk us through your resume, highlighting your experience relevant to the role you are applying for at D.E. Shaw? (Background & Experience)

How to Answer:
When answering this question, focus on your experiences that align with the job description. Highlight your technical skills, any relevant projects, and experiences that show you possess the qualities that D.E. Shaw looks for in a candidate. Be succinct and structure your answer in a way that tells a coherent story of your professional journey.

My Answer:
Certainly! I have a background in computer science with a focus on algorithm design and data analysis, which I believe aligns well with the analytical and quantitative approach at D.E. Shaw.

• Education: I earned my Bachelor’s degree in Computer Science from XYZ University, where I focused on algorithms and machine learning. My coursework included advanced mathematics, statistical analysis, and software development.
• Professional Experience:
  • Software Developer at ABC Corp (2018-2020): Developed high-performance trading systems, optimized for speed and reliability. Worked on a team applying quantitative models to real-time trading data.
  • Data Analyst at XYZ Data (2020-Present): Analyzed large datasets to uncover trends and inform investment strategies. Developed predictive models using machine learning techniques.
• Relevant Projects:
  • Algorithmic Trading Model: For my senior thesis, I developed an algorithmic trading model that simulated market conditions and tested various trading strategies.
  • Machine Learning for Financial Forecasting: At XYZ Data, I led a project that utilized machine learning to forecast stock performance based on historical data and market indicators.

This experience has equipped me with the analytical skills and technical knowledge that are vital for a role at D.E. Shaw. I am particularly adept at applying computational techniques to solve complex problems, which I understand is at the heart of what D.E. Shaw does.

Q2. Why do you want to work at D.E. Shaw, and what interests you about our company? (Cultural Fit & Motivation)

How to Answer:
Your answer should reflect your knowledge of the company and its culture, as well as how your personal values and career goals align with the organization. Mention specific aspects of the company that appeal to you, such as its reputation for innovation, its commitment to teamwork, or the opportunity to work on challenging projects.

My Answer:
I am impressed by D.E. Shaw’s reputation for combining rigorous quantitative analysis with cutting-edge technology to tackle complex financial challenges. What excites me about the company are:

• Innovative Culture: The firm’s commitment to innovation and continuous learning resonates with my desire to work in an environment that encourages creative problem-solving.
• Interdisciplinary Approach: I am drawn to the interdisciplinary nature of the work at D.E. Shaw, which aligns with my own background in computer science and analytics.
• Impactful Work: The opportunity to contribute to a firm that has a significant impact on the financial markets is particularly appealing to me.

I believe that D.E. Shaw provides the ideal setting for me to leverage my skills and grow professionally, and I am eager to be part of a team that values collaboration and excellence.

Q3. How do you approach problem-solving, and can you provide an example of a challenging problem you’ve solved? (Problem-Solving Skills)

How to Answer:
Demonstrate your problem-solving methodology with a structured approach, such as defining the problem, analyzing the causes, exploring solutions, implementing a strategy, and reviewing the results. Provide a specific example that shows your ability to tackle complex issues effectively.

My Answer:
My problem-solving approach is analytical and systematic. Here’s how I typically tackle problems:

1. Define the problem: I start by understanding the problem in depth.
2. Gather information: I collect relevant data and insights to inform my understanding.
3. Brainstorm solutions: I consider various approaches and weigh their potential effectiveness.
4. Execute a plan: I implement the most promising solution, ensuring that it is executed meticulously.
5. Review: I analyze the outcome to learn from the experience and improve future problem-solving.

Example:
At XYZ Data, we faced a challenge where our predictive models were not accurately forecasting stock prices. I led the effort to diagnose the issue, which involved:

• Data Analysis: Discovered that our models were overfitting due to noisy data.
• Solution Development: Proposed using a more robust validation framework and incorporating additional regularization techniques.
• Implementation: Redesigned our machine learning pipeline to include these improvements.
• Outcome: The new models showed a 20% improvement in predictive accuracy on out-of-sample data.

This experience highlighted the importance of rigorous validation in machine learning and the need to constantly refine our approaches.

Q4. Describe a time when you had to work with a difficult team member and how you handled the situation. (Teamwork & Interpersonal Skills)

How to Answer:
Recall a situation where you demonstrated empathy, communication skills, and the ability to navigate interpersonal conflicts. Your answer should convey your capacity to maintain a professional demeanor and work collaboratively, even in challenging circumstances.

My Answer:
At ABC Corp, I worked with a team member who was brilliant but often dismissive of others’ ideas, which led to tension within the team. I handled the situation by:

• Empathy: I took the time to understand his perspective and acknowledged the value he brought to the team.
• Communication: I initiated a private conversation where I expressed my concerns in a non-confrontational manner and suggested we collaborate more openly.
• Collaboration: We agreed to jointly lead a project, which improved our working relationship and the team dynamics.

This experience taught me the importance of direct and empathetic communication in resolving interpersonal issues.

Q5. Explain the concept of a ‘P-Value’ in statistics and its relevance in data analysis. (Technical Knowledge – Statistics)

A ‘P-Value’, or probability value, is a statistical measure that helps researchers determine the significance of their results. It is the probability of observing results at least as extreme as those in your data, under the assumption that the null hypothesis is true.

The relevance of a P-Value in data analysis can be broken down as follows:

• Hypothesis Testing: It is used in the context of hypothesis testing to quantify the evidence against the null hypothesis. A low P-Value indicates that the observed data is unlikely under the null hypothesis, suggesting that the alternative hypothesis may be true.
• Significance Levels: Often, a P-Value is compared to a predefined significance level (commonly 0.05) to decide whether to reject the null hypothesis. If P < 0.05, researchers may conclude that the results are statistically significant.
• Decision Making: In data-driven industries like finance, P-Values guide decision-making by indicating the reliability of an observed effect or relationship.

Here is an example of how a P-Value might be interpreted in a table:

Experiment	P-Value	Statistical Significance
A	0.03	Yes
B	0.20	No
C	0.01	Yes
D	0.50	No

In this table, Experiments A and C have P-Values below 0.05, suggesting their results are statistically significant, while Experiments B and D do not show statistical significance.

Q6. How do you ensure the quality and accuracy of your work when dealing with large datasets? (Data Analysis & Attention to Detail)

When dealing with large datasets, ensuring quality and accuracy is paramount. Here are some strategies I use:

• Data Cleaning: I begin by cleaning the data, removing or correcting any inaccuracies, inconsistencies, or irrelevant data points.
• Validation Rules: Setting up validation rules helps to prevent incorrect data entry at the source.
• Automated Scripts: I use automated scripts to check for common errors or anomalies in the data.
• Statistical Analysis: Employing statistical methods can help in identifying outliers or patterns that may indicate data quality issues.
• Cross-Referencing: When possible, I cross-reference the data with other trusted sources to ensure reliability.
• Version Control: Maintaining versions and changes of the dataset is essential to track what has been modified and why.
• Peer Review: Having a colleague review my work can help catch issues that I might have missed.

Q7. What programming languages are you proficient in, and which do you prefer for data analysis? (Technical Knowledge – Programming)

I am proficient in several programming languages including:

• Python: Preferred for data analysis due to its rich ecosystem of libraries such as Pandas, NumPy, and scikit-learn.
• R: Also great for statistical analysis and visualization, though I tend to prefer Python for its versatility.
• SQL: Essential for data extraction and interaction with databases.

For data analysis, I prefer Python because of its readability and the extensive support from the data science community.

Q8. Discuss a project where you applied machine learning techniques, and what was the outcome? (Technical Knowledge – Machine Learning)

How to Answer:
Outline the project’s objective, the machine learning techniques used, any challenges faced, and the results achieved.

My Answer:
In my previous role, I worked on a project to predict customer churn for a telecom company. We used several machine learning techniques including logistic regression, random forests, and gradient boosting machines.

• Objective: To reduce customer churn by accurately predicting which customers were likely to leave.
• Techniques: Used logistic regression for baseline, random forests for capturing non-linear relationships, and gradient boosting for improving predictive performance.
• Challenges: The imbalanced dataset was a significant challenge, which we addressed through SMOTE (Synthetic Minority Over-sampling Technique).
• Outcome: The gradient boosting model outperformed the others with a precision-recall AUC of 0.85, and the insights were used to inform customer retention strategies.

Q9. What experience do you have with financial modeling, and what tools do you use for it? (Technical Knowledge – Finance)

I have experience with financial modeling in several contexts, including valuations, scenario analysis, and risk assessments. The tools I commonly use include:

• Excel: For traditional financial modeling tasks due to its powerful formulae and pivot tables.
• Python: For more complex models that require automation or handling large datasets with libraries like pandas and NumPy.
• Matlab: Occasionally, for when complex quantitative finance models are needed.

Q10. How do you stay current with the latest developments in the field of quantitative finance? (Continuous Learning & Industry Knowledge)

To stay current with the latest developments in quantitative finance, I:

• Attend industry conferences and seminars.
• Regularly read relevant journals such as The Journal of Finance and Quantitative Finance.
• Participate in online communities and forums like Quantopian.
• Enroll in ongoing education courses and professional development programs.
• Network with peers to share knowledge and learn about new techniques and tools.

Here is a list of ways I stay current:

• Conferences: Attend at least one major conference yearly, such as the Quantitative Finance Conference.
• Journals: Subscribe to and read articles regularly from key journals.
• Forums: Actively participate in online forums and discussions.
• Education: Take at least two online courses or workshops each year to learn new skills.
• Networking: Engage in networking events to exchange ideas with industry professionals.

Q11. Describe your experience with risk management and how you would mitigate risks in a portfolio. (Risk Management)

How to Answer:
When tackling this question, be sure to highlight your understanding of various risk management techniques, tools, and strategies. Present your answer by explaining specific experiences where you have implemented risk management measures. You can talk about your familiarity with financial models, portfolio diversification, hedge strategies, and use of financial instruments to mitigate risks.

My Answer:
In my previous role as a portfolio manager, I was responsible for overseeing a diversified asset portfolio. My experience with risk management primarily revolves around the following strategies:

• Portfolio Diversification: I ensured that the portfolio was well-diversified across various asset classes and industries to minimize the impact of sector-specific downturns.
• Financial Modeling: Utilizing quantitative models, I regularly assessed the portfolio’s value at risk (VaR) to understand the potential for loss and adjust the investments accordingly.
• Hedge Strategies: I often used derivatives like options and futures contracts to hedge against market volatility and protect the portfolio’s value.
• Stop-Loss Orders: To mitigate the risks of significant price movements, I employed stop-loss orders to automatically sell assets at a predetermined price level.
• Continuous Monitoring: I also implemented a robust risk management system for continuous monitoring and reporting, which allowed for timely adjustments based on market conditions.

Q12. Can you explain what a derivative is and give an example of where it might be used? (Technical Knowledge – Financial Instruments)

A derivative is a financial instrument whose value is derived from the performance of an underlying asset, index, or interest rate. They are primarily used for hedging risks, speculating on future price movements, or gaining access to otherwise inaccessible markets or assets.

Example:
An example of a derivative is a futures contract, which is an agreement to buy or sell an asset at a future date for a fixed price. Futures are commonly used by farmers to lock in the price of their crop ahead of the harvest. This practice helps them hedge against the risk of price fluctuations due to unforeseen events affecting supply and demand.

Q13. How would you handle a situation where you are given a tight deadline for a complex analysis? (Time Management & Stress Handling)

How to Answer:
In responding to this question, discuss your ability to prioritize tasks, manage time effectively, and maintain quality work under pressure. Explain your approach to breaking down complex tasks into manageable parts and your experience in collaborating with others to meet tight deadlines.

My Answer:
When faced with a tight deadline for a complex analysis, I take the following steps:

• Prioritize Tasks: I quickly prioritize the components of the analysis based on their importance and the time required to complete them.
• Break Down the Project: I break down the complex analysis into smaller, more manageable tasks, which helps in tracking progress and ensuring that critical aspects are covered.
• Leverage Teamwork: If possible, I distribute tasks among team members, playing to each person’s strengths to increase efficiency.
• Effective Communication: I maintain clear communication with stakeholders, providing updates and setting realistic expectations about what can be delivered within the given timeframe.
• Focus and Efficiency: I minimize distractions and focus intently on the task at hand, often working in sprints to maintain a high level of efficiency.

Q14. Provide an example of a time when you had to present complex data to a non-technical audience. How did you ensure clarity and comprehension? (Communication Skills)

How to Answer:
You should answer this question by giving a specific instance where you successfully translated complex information to a lay audience. Detail your approach to making the data digestible, such as simplifying terminology, using visual aids, and checking for understanding.

My Answer:
At my previous job, I was tasked with presenting the results of a complex financial analysis to a group of stakeholders with varied backgrounds. To ensure clarity and comprehension, I:

• Simplified Terminology: Avoided jargon and technical terms, using plain language instead.
• Visual Aids: Created charts and graphs to represent the data visually, making it easier to grasp.
• Storytelling: Framed the data within a narrative that highlighted the key points and relevance to the audience’s interests.
• Engagement: Asked questions and encouraged feedback throughout the presentation to gauge understanding and keep the audience engaged.
• Follow-Up: Provided a one-page summary with the main takeaways and offered to answer any further questions after the presentation.

Q15. In your opinion, what are the key traits of a successful trader, and do you possess these traits? (Self-Assessment & Trade Skills)

How to Answer:
Reflect on the personal qualities and professional skills that enable a trader to perform well in the fast-paced and often high-stress trading environment. Assess how you exemplify these traits based on your past experiences and successes.

My Answer:
In my view, the key traits of a successful trader include:

• Analytical Skills: The ability to process and analyze large amounts of data to make informed trading decisions.
• Discipline: Sticking to a trading plan and not letting emotions drive decisions.
• Decisiveness: Being able to make quick decisions in response to market movements.
• Risk Management: Understanding how to manage and mitigate risk effectively.
• Adaptability: The capacity to adapt to new information and changing market conditions.

I believe I possess these traits, as evidenced by my track record of making sound trading decisions based on thorough analysis. My disciplined approach has allowed me to stay focused on long-term strategies while my adaptability has enabled me to capitalize on emerging market opportunities.

Q16. Can you discuss a time when you identified a new opportunity or inefficiency in the market? (Innovation & Market Understanding)

How to Answer

When answering this question, focus on demonstrating your analytical skills, market knowledge, and innovation capabilities. Be specific about the opportunity or inefficiency you identified, explain the approach you took to analyze the situation, and describe the steps you took or would take to capitalize on the opportunity or rectify the inefficiency. Employers are looking for evidence that you can identify and act on market signals in a way that benefits the company.

My Answer

Yes, I can share an instance from my previous role where I identified an inefficiency in the market. While analyzing the performance of mid-cap stocks, I noticed that certain sectors were consistently outperforming the market during specific economic indicators, yet were underrepresented in investment portfolios.

• Identification of Opportunity: Conducted a thorough analysis to understand the underlying factors driving mid-cap outperformance.
• Analysis: Used statistical models to validate the consistency and predictability of the performance across different market cycles.
• Action Taken: Proposed a new investment strategy that recommended a higher allocation to these mid-cap sectors.

This approach not only demonstrated an innovative way to enhance portfolio returns but also contributed to better risk-adjusted performance for our clients.

Q17. What is your understanding of algorithmic trading, and have you ever developed or improved an algorithm? (Technical Knowledge – Algorithmic Trading)

Algorithmic trading is the process of using computer programs to execute trades according to pre-defined criteria, such as timing, price, and volume, with minimal human intervention. Algorithms are designed to make trading more efficient and to capitalize on market opportunities faster than human traders could.

I have developed and improved trading algorithms during my career. For instance, I created a mean-reversion trading strategy based on the hypothesis that prices and returns eventually move back towards the mean or average.

def mean_reversion_strategy(prices, window, z_threshold):
    # Calculate the moving average and standard deviation
    moving_average = prices.rolling(window=window).mean()
    moving_std = prices.rolling(window=window).std()
    
    # Calculate the z-score for each point in time
    z_score = (prices - moving_average) / moving_std
    
    # Generate signals based on z-score
    signals = (z_score > z_threshold).astype(int) - (z_score < -z_threshold).astype(int)
    return signals

• Improvement: After backtesting, I fine-tuned the parameters and included a stop-loss mechanism to enhance the risk-adjusted returns.

Q18. How would you approach building a diversified investment portfolio? (Investment Strategy)

Building a diversified investment portfolio requires understanding the investor’s risk tolerance, investment horizon, and financial goals. Here is a step-by-step approach:

• Assess Risk Tolerance: Determine the level of risk the investor is willing to take.
• Define Investment Goals: Set clear, measurable, and attainable investment goals.
• Asset Allocation: Choose a mix of asset classes (e.g., stocks, bonds, real estate) that aligns with the risk tolerance and goals.
• Selection of Investments: Within each asset class, select individual investments (e.g., specific stocks, sectors, or funds).
• Regular Rebalancing: Periodically adjust the portfolio to maintain the desired asset allocation.
• Monitoring and Review: Continuously monitor the performance and re-evaluate the investment strategy as necessary.

Q19. What methods do you use to assess and manage the performance of a trading strategy? (Performance Evaluation)

To assess and manage the performance of a trading strategy, I use various quantitative methods:

• Backtesting: Running the strategy against historical data to assess its viability.
• Benchmarking: Comparing the strategy’s performance to relevant benchmarks.
• Risk-Adjusted Return Metrics: Using metrics like Sharpe Ratio, Sortino Ratio, or Alpha to understand performance relative to the risk taken.
• Drawdown Analysis: Evaluating the maximum loss from a peak to a trough of a portfolio.
• Stress Testing: Analyzing how the strategy performs under extreme market conditions.

Managing the performance involves continuous optimization of the strategy, including adjustments in response to market changes, risk management improvements, and refinement of the trading algorithms.

Q20. Explain a scenario where you used data visualization to influence a business decision. (Data Visualization & Decision Making)

In a previous role, I used data visualization to influence a decision regarding the allocation of marketing resources.

Scenario: Sales were declining in certain regions, but the data was complex and difficult for stakeholders to understand.

Action: I created a series of heat maps showing sales performance by region over time and combined this with line charts that highlighted trends in marketing spend.

Outcome: The visualizations made it clear that regions with reduced marketing spend were seeing a drop in sales. This influenced the decision to reallocate marketing resources, which subsequently led to a turnaround in sales trends.

Q21. How do you prioritize tasks when working on multiple projects simultaneously? (Project Management & Prioritization)

How to Answer:
When answering this question, it helps to demonstrate your ability to manage time effectively, use prioritization frameworks, and communicate with stakeholders. Mention specific tools or methods you use, like the Eisenhower Matrix, Agile methodologies, or any project management software.

My Answer:
To prioritize tasks effectively when juggling multiple projects, I typically follow these steps:

• Assess Urgency and Importance: I evaluate tasks based on their urgency and importance, often using the Eisenhower Matrix. Tasks that are urgent and important take priority.
• Understand Deadlines: I make sure to be aware of all deadlines and schedule my work accordingly to meet them without compromising quality.
• Communicate with Stakeholders: Regular communication with project stakeholders helps me to align my priorities with their expectations and project goals.
• Use Project Management Tools: Tools like Jira, Trello, or Asana help me organize tasks, track progress, and adjust priorities as needed.
• Review and Adapt: Priorities can change, so I regularly review my task list and adapt as needed to ensure I am working on the most critical tasks at any given moment.

Q22. Describe your experience with backtesting trading strategies and the tools you use for it. (Technical Knowledge – Backtesting)

In my experience, backtesting is crucial for evaluating the viability of trading strategies. My approach to backtesting includes:

• Historical Data Analysis: I collect historical market data relevant to the strategy’s asset class.
• Strategy Coding: I typically code trading strategies using Python, leveraging libraries like pandas and NumPy for data manipulation.
• Risk Management Considerations: Incorporating risk management parameters like stop-loss orders and position sizing is essential for realistic simulation.
• Performance Metrics: I analyze various performance metrics like Sharpe ratio, maximum drawdown, and profit factor.
• Iterative Testing: Strategies often require refinements, so I conduct multiple rounds of testing to tweak parameters and improve performance.
• Tools: I primarily use QuantConnect and Backtrader as backtesting platforms for their flexibility and comprehensive data sets.

Q23. Can you give an example of how you have contributed to a team project and what role you played? (Teamwork & Contribution)

How to Answer:
Your answer should include a specific example that showcases your ability to work collaboratively, take the initiative, and contribute to a group effort. Be clear about the role you played and the impact your contribution had on the project’s outcome.

My Answer:
In my previous role, I contributed to a team project focused on developing a proprietary trading algorithm. My role was to:

• Research and Development: I conducted extensive research on market indicators and designed algorithmic models based on findings.
• Collaboration: Worked closely with the data science team to refine the model using machine learning techniques.
• Testing: I took the lead on backtesting the strategy, ensuring its robustness and viability before deployment.
• Documentation: I documented all processes and findings to enable knowledge sharing within the team.
• Result: The project resulted in an algorithm that outperformed our benchmarks, contributing substantially to the firm’s trading success.

Q24. What strategies do you employ to handle high-pressure situations in the workplace, particularly during market volatility? (Stress Management)

My strategies for managing high-pressure situations, especially during market volatility, include:

• Staying Informed: Keeping abreast of market trends and news helps me anticipate volatility and prepare responses.
• Planning and Scenario Analysis: Having contingency plans in place for various market scenarios allows for quicker decision-making under pressure.
• Mindfulness and Stress Reduction Techniques: Practicing mindfulness and employing stress-reduction techniques like deep-breathing exercises helps maintain a clear head.
• Regular Breaks: Taking short, regular breaks throughout the day helps prevent burnout and maintain productivity.
• Emotional Regulation: I focus on maintaining emotional composure, understanding that knee-jerk reactions are often counterproductive in volatile market conditions.

Q25. How do you evaluate the ethical implications of investment decisions, and can you provide an example of how you have navigated an ethical dilemma? (Ethics & Decision Making)

How to Answer:
Discuss your approach to ethical decision-making, emphasizing the importance of integrity, transparency, and compliance with both legal standards and company policies. Share a real-life example where you faced an ethical challenge and how you resolved it.

My Answer:
To evaluate the ethical implications of investment decisions, I consider:

• Compliance with Laws and Regulations: Ensuring all decisions adhere to the relevant legal standards.
• Alignment with Company Values: Decisions must reflect the company’s core values and ethical principles.
• Transparency: Maintaining transparency with stakeholders about investment risks and potential conflicts of interest.
• Long-term Impact: Assessing the long-term effects on stakeholders, the environment, and society.

Example: I once faced an ethical dilemma when I discovered that an attractive investment opportunity involved a company with questionable labor practices. Despite the potential financial gain, I recommended against the investment because it conflicted with our firm’s commitment to social responsibility. We instead sought alternative investments aligned with our ethical standards.

4. Tips for Preparation

Before stepping into the interview room, ensure you’ve done a thorough research about D.E. Shaw’s culture, values, and recent news or developments. This will enable you to tailor your responses to align with the company’s ethos and show genuine interest.

Brush up on your technical skills, especially those listed in the job description. Be prepared to solve problems on the spot or explain complex concepts succinctly. Additionally, rehearse speaking about your experiences clearly and confidently, focusing on how your skills match the role’s requirements.

5. During & After the Interview

During the interview, be concise yet informative in your responses. Interviewers will likely seek evidence of your critical thinking, adaptability, and how you function under stress. Be honest, especially when discussing challenges or gaps in your knowledge – it’s an opportunity to show your willingness to learn.

Avoid common pitfalls such as speaking negatively about past employers or colleagues. Remember, non-verbal communication also matters; maintain eye contact and show enthusiasm. Before concluding, ask insightful questions to demonstrate your interest and understanding of the role.

Post-interview, send a personalized thank-you email to express your appreciation for the opportunity. It’s polite and keeps you top of mind. As for feedback, D.E. Shaw typically informs candidates about next steps within a few weeks, so be patient but proactive in your follow-up.