Robinhood Marketing Inc. is a financial service company that designs mobile and web application software “catering to cash management systems, such as stocks, exchange-traded funds, options, and cryptocurrency”. Unlike other financial institutions, Robinhood has no storefront branches, as they operate entirely online with zero operation fees and very low minimum capital investment. Founded in April of 2013, the company now has over one thousand employees across the US, growing from a starting user base of 500,000 to over thirteen million users.

As of 2019, Robinhood has completed over $150 billion in transactions and made over $300 million in revenue within the first two quarters of 2020. Given the volume of trade completed and the activity of millions of users, it is not unrealistic to imagine that Robinhood generates massive amounts of data– a treasure trove for data scientists, analysts, and data engineers to grow. This multifaceted data stream (rapidly changing market data, user data based on app activity, and brokerage operations data) is integrated seamlessly via robust data architecture to perfect processes and workflows.

The Data Scientist Role at Robinhood

Data scientist roles at Robinhood cover domain-wide expertise. These roles may range from performing standard analytics, such as experimentation, A/B testing, and dashboard development, to more advanced machine learning techniques, like classification, logistic regression, decision trees, and deep learning techniques. Roles can also be tailored to teams or specifically assigned projects.

Required Skills

The Robinhood Data Scientist Interview requires several strong skills in programming, SQL, and statistics.
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Every decision at Robinhood is backed by data. Because it is so integral, the company prefers to hire candidates with a minimum of two years of industry experience working with data or data analytics related projects.

Other relevant qualifications include:

  • Strong programming skills, especially in scripting languages such as R, Python, etc.
  • Bachelor’s/Master’s degree in Mathematics, Statistics, Economics, Engineering, Natural Sciences, or quantitative fields.
  • Over three years of experience writing SQL queries.
  • Sound understanding of the statistical methodologies, including experimental designs, and A/B testing frameworks.
  • Deep understanding of machine learning methodologies, especially prediction/binary classification, logistic regression, decision trees, and deep learning techniques.
  • Basic understanding of the distributed system for processing large-scale data streams into useful applications.
  • Experience with deploying machine learning algorithms and developing a system for tracking data integrity.

Data Scientist Teams at Robinhood

The data science team forms the central node to every other team within Robinhood. Even as teams expand across different organizational levels, data scientists within the Data Science team or other internal teams remain a critical part of business-impact decisions made at Robinhood.

Depending on the team, assigned responsibilities may include:

  • Data Science: Prototyping machine learning systems to power analytics efforts, adapting machine learning algorithms to facilitate problem-solving across multiple internal teams, building statistical models to predict user engagement, and improving existing statistical methodologies to reinforce Robinhood's experimentation platform.
  • Insights and Intelligence Team: Leveraging data and data analytics to help build customer-centric culture and contributing to a strong data analytics culture.
  • Machine Learning Engineering Team: Identifying critical problems that have solutions within the scope of machine learning, and then designing and implementing these solutions across every product level. Other responsibilities also include collaborating with several internal teams, including data infrastructure, product, growth, fraud and risk engineering teams to drive growth.

Interview Process

The Robinhood Data Scientist Interview comprises of two screenings, a take-home challenge, and an onsite interview.
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This is a pretty standard interview process. The entire hiring process for data scientist roles at Robinhood comprises three interview stages, including the initial phone call interview with either HR or a hiring manager.

Initial Screen

As stated above, the introductory stage is usually conducted by HR or a hiring manager, generally lasting around 30 minutes. The discussion in this interview will revolve around the length and breadth of your past relevant projects and your data science experiences as they align with the job role.

Take-Home Data Challenge

Before the technical screening, there’s usually a 48 hour take-home challenge. This assignment consists of six questions that can include basic probability, ML questions, and some open-ended classification problems, along with a case-study challenge, where candidates are expected to predict churns from data provided.

For more insights into how to approach data science case studies, check out our article here.

Technical Screen

This is a one hour long interview with a data scientist. There are usually some coding questions asked, and the remainder of the time will be spent discussing one of your  past projects that relates to the job role you are applying for.

Onsite Interview

This interview contains four back-to-back 45 minute interview sections. Questions at this stage are usually case-based and open-ended. The general data scientist onsite interview looks like this:

  • Case-based and open-ended data science and statistics challenge.
  • One whiteboard programming/coding interview with a team leader.
  • Computer programming challenge: you will be provided with a laptop to write code.
  • Another case-based and open-ended data science interview with a data scientist.

Note: There is usually a fifth interview with top executives at Robinhood. This is a formal meeting where you get to discuss the company’s mission and goals.

Looking for more unique interview guides and questions? Check out our articles on the Zoom Data Scientist Interview and Facebook Data Science Interview Questions!

Notes and Tips

To better understand users and market trends, Robinhood employs the most advanced data analytics and machine learning technology on all its market data, user data, and brokerage operations data. As a result of this, the Robinhood Data Scientist interview covers the entire length and breadth of data science, as well as behavioral and product sense knowledge. One of the major aims of the Robinhood Data Scientist interview process is to assess candidates’ skills and knowledge in machine learning theories and techniques, algorithms, and product sense.

Questions are usually open-ended and case-based to reflect real-life situations at Robinhood to a greater degree. Skills tested include statistics and probability (such as hypothesis testing, logistic regression models, etc.), A/B testing, SQL, Python (string manipulation, array, etc.), machine learning theories (churn prediction and modelling), and predictive algorithms.

Remember to brush up your statistics and basic probability knowledge. For the more technical aspects of the interview, visit interviewquery.com and practice lots of Robinhood Data Scientist questions.

Robinhood Data Scientist Interview Questions

  • Assume you have a square grid of known size and each spot in a grid represents a number of some real value. If you start at the top left corner and can only go down or right, what is the maximum number you can obtain once you reach the bottom left?
  • Assume you have a logistic model that is heavily weighted on one variable and that one variable has a dataset like 50.00, 100.00, 40.00, etc. Next, assume that there was a data quality issue with that variable and an unknown number of values removed the decimal point (e.g. 100.00 turned into 10000). Would the model be valued? Why or why not? How would you fix the model?
  • How could you figure out how spending on a billboard was working with multiple other variables at play?
  • How do you interpret the coefficients of a logistic regression model?
  • How do you define good investors? How do you identify them?
  • What is churn? How can we predict if a person is churning?
  • Given an array of numbers that represents heights of 2-D Mountains, compute the amount of water stored after the rainfall among those mountains.

Build a model to predict whether a user will cancel an account.

Tax Basis

It's tax season! Given a set of transactions, find out the cost basis for each sell and compute the overall capital gain/loss. The cost basis for a sold equity is the price at which the equity being sold was bought at.

You are provided with a sorted list of tuples, each of which represents a transaction. These tuples are formatted as follows: (date: int, symbol: string, side: string (buy/sell), quantity: int, price: float).

For each sell, output the following information: (symbol: string, date_bought: int, date_sold: int, price_bought_at: float, price_sold_at, quantity: int, capital_gain: float). Finally, output the overall capital gain/loss.

Disclaimer: The material contained herein is for whimsical purposes only and does not constitute tax advice. Investors should consult with their own tax advisor or attorney with regard to their personal tax situation.

Part I

Use FIFO cost basis strategy

Example:

Input:

transactions = [

{'date': 1, 'symbol': 'FB', 'side': 'buy', 'quantity': 1, 'price': 200.00},

{'date': 3, 'symbol': 'AAPL', 'side': 'buy', 'quantity': 2, 'price': 100.00},

{'date': 4, 'symbol': 'FB', 'side': 'sell', 'quantity': 1, 'price': 150.00},

{'date': 6, 'symbol': 'AAPL', 'side': 'buy', 'quantity': 1, 'price': 150.00},

{'date': 7, 'symbol': 'AAPL', 'side': 'sell', 'quantity': 1, 'price': 200.00},

{'date': 8, 'symbol': 'AAPL', 'side': 'buy', 'quantity': 4, 'price': 210.00},

{'date': 11, 'symbol': 'AAPL', 'side': 'sell', 'quantity': 4, 'price': 220.00},

]

Output:

[

(symbol: 'FB', date_bought: 1, date_sold: 4, price_bought_at: 200.00, price_sold_at: 150.00, quantity: 1, capital_gain: -50.00),

(symbol: 'AAPL', date_bought: 3, date_sold: 7, price_bought_at: 100.00, price_sold_at: 200.00, quantity: 1, capital_gain: 100.00),

(symbol: 'AAPL', date_bought: 3, date_sold: 11, price_bought_at: 100.00, price_sold_at: 220.00, quantity: 1, capital_gain: 120.00),

(symbol: 'AAPL', date_bought: 6, date_sold: 11, price_bought_at: 150.00, price_sold_at: 220.00, quantity: 1, capital_gain: 70.00),

(symbol: 'AAPL', date_bought: 8, date_sold: 11, price_bought_at: 210.00, price_sold_at: 220.00, quantity: 2, capital_gain: 20),

],

260.00


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