Mission: To bring community and belonging to everyone in the world.
Act like an owner: Our mission is larger than what any individual can accomplish on their own. Think beyond yourself and your team. When making decisions, put Reddit’s mission first.
Bias towards action: Much of our work is not predictable, which means we need to be not only flexible, but also aggressive in taking on new challenges as they arise. Instead of debating the perfect owner or waiting for the perfect solution, be the owner and take action.
Evolve: The world is constantly evolving, and we need to evolve with it. Only by continually improving, learning, and taking calculated risks to find new opportunities—both for the business and for ourselves as individuals—will we succeed.
Keep reddit real: Reddit is a vast, vibrant, diverse, and sometimes weird place. We celebrate diversity, both in our communities and in our company.
Remember the human: We treat others the way we’d like to be treated, and we remember our actions and words affect real people, sometimes in ways that we cannot see. We understand that Reddit plays a significant role in people’s lives, and it’s our duty to provide them a quality service and safe experience.
What would you look at to increase user growth and retention?
Explain the difference between L1 and L2 regularization methods?
Tell me the difference between an inner join, left join/right join, and union.
Estimate the number of "Happy Birthday" posts that are logged on Meta everyday.
You have a data set containing 100K rows, and 100 columns, with one of those columns being our dependent variable for a problem we"d like to solve. How can we quickly identify which columns will be helpful in predicting the dependent variable? Identify two techniques and explain them to me as though I were 5 years old.
What is the central limit theorem and why is it important in data science?
What are your 3 favorite data visualization techniques?
How do you handle missing data?
Explain the 80/20 rule, and tell me about its importance in model validation.
In your opinion, which is more important when designing a machine learning model: model performance or model accuracy?
What is one way that you would handle an imbalanced data set that"s being used for prediction? (i.e. vastly more negative classes than positive classes.)
Explain the following parts of a linear regression to me: p-value, coefficient, R-Squared value. What is the significance of each of these components and what assumptions do we hold when creating a linear regression?
Given tweets and Meta statuses surrounding a new movie that was recently released, how will you determine the public"s reaction to the movie?
I have two models of comparable accuracy and computational performance. Which one should I choose for production and why?
Stage 1: Phone screen with recruiter
Stage 2: Technical interview with live coding
Stage 3: Onsite interview
Pathrise is a career accelerator that helps people land their dream jobs. We regularly place our fellows at top companies like Apple, Amazon, and Meta. Our mentors have experience at companies like Apple, giving fellows the inside scoop on interview and company culture in 1-on-1 sessions.
We can’t guarantee you a job at a specific company like Apple. But we do guarantee you a great job–if you don’t accept an offer in 1 year, you pay nothing. Our income share agreement means you only pay with a percentage of your income at your new role.
Mentors work with fellows at every stage in search, helping them build the skills necessary to be the best candidate possible. Fellows in Pathrise usually see a 2-4x increase in application response rates, 1.5-3x increase in interview scores, and 10-20% increase in salary through negotiation.