The 1:1 mentorship package helped me so much get a data scientist job at Facebook!Menghan Xu

1:1 mentorship package has led to offers at:

I bought the 1:1 mentorship package and as a result I got a job as a Data Scientist at Pinterest! Dorna Bandari

Real data science!

Data Challenges

Practice in advance on challenges just like the ones you will get in the interview, resembling what you will actually do at work

Product On-Site Questions

Learn how to answer the hardest product, metric, A/B testing on-site questions. Such as: Should we add the love button on FB?, Which metric would you choose for this new feature?, etc.

SQL

SQL exercises just like what you will get in the on-site or shared-screen interview

1:1 Coaching

Personalized mentorship from book author on how to tackle data challenges and answer product questions

The phone call to discuss in advance onsite questions was so useful to get a DS job at Booking.com! Doudou Tang

Comprehensive Interview Material

$299

  • ☑ [ebook] DS takehome challenges: 20 take-home challenges with real world data sets. 4 challenges have detailed solutions with reusable step by step approach. See here for challenge samples
  • ☑ [ebook] DS product questions: 40 product, metric, A/B testing on-site interview questions w/ answers. See here for answer samples and list of all questions
  • 6 SQL problems with solutions modeled after top tech company SQL shared screen interview step. See here for samples
  • 1:1 feedback - 1 challenge:
    Send your solution for 1 challenge of your choice. You'll get a detailed feedback on your work

1:1 Mentorship

$699

  • Everything in the Comprehensive Interview Material package
  • Unlimited 1:1 feedback - All challenges
    Send your solution for all challenges. You'll get a detailed feedback on your work
  • ☑ 1 phone call with book author to discuss how to answer interview questions and/or simulate shared screen coding interview using real questions
  • ☑ After you complete two challenges, you will receive a list of data science hiring manager contact information from all top US tech companies. See here for details.
  • ☑ Will review your resume and make it perfect to apply for data science jobs

Hiring Manager Contact Info

As part of the 1:1 mentorship package, you will unlock this table after completing two challenges. The table provides contact information of data science hiring managers and HR. Search through the table to see if representatives from your target companies are there. If not, email us at info@datamasked.com to ask to add them.

 

Company
Name
Email
Linkedin_profile
Role (Hiring Manager or HR)
Last_verified (mm/dd/yy)
Adrollxxx yyyyxxxxx@adroll.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
AIGxxx yyyyxxxxx@aig.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Airbnbxxx yyyyxxxxx@airbnb.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Akamaixxx yyyyxxxxx@akamai.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Amazonxxx yyyyxxxxx@amazon.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Amazonxxx yyyyxxxxx@amazon.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Amazonxxx yyyyxxxxx@amazon.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Applexxx yyyyxxxxx@apple.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Applexxx yyyyxxxxx@apple.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Applexxx yyyyxxxxx@apple.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Applexxx yyyyxxxxx@apple.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
BNY Mellonxxx yyyyxxxxx@bnymellon.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Boschxxx yyyyxxxxx@us.bosch.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Capital Onexxx yyyyxxxxx@capitalone.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Capital Onexxx yyyyxxxxx@capitalone.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Capital Onexxx yyyyxxxxx@capitalone.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Capital Onexxx yyyyxxxxx@capitalone.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Capital One Home Loansxxx yyyyxxxxx@capitalone.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Cheggxxx yyyyxxxxx@chegg.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Cruise Automationxxx yyyyxxxxx@getcruise.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Facebookxxx yyyyxxxxx@fb.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Facebookxxx yyyyxxxxx@fb.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Fidelity Investmentsxxx yyyyxxxxx@fidelity.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Fitbitxxx yyyyxxxxx@fitbit.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Fitbitxxx yyyyxxxxx@fitbit.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
GE Healthcarexxx yyyyxxxxx@ge.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
GE Power Digital Solutionsxxx yyyyxxxxx@ge.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Goldman Sachsxxx yyyyxxxxx@gs.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Googlexxx yyyyxxxxx@google.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Googlexxx yyyyxxxxx@google.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Googlexxx yyyyxxxxx@google.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
GSNxxx yyyyxxxxx@gsn.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
HERExxx yyyyxxxxx@here.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
IBMxxx yyyyxxxxx@us.ibm.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Infosysxxx yyyyxxxxx@infosys.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
JPMorgan Chasexxx yyyyxxxxx@jpmorgan.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Kabbagexxx yyyyxxxxx@kabbage.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Linkedinxxx yyyyxxxxx@linkedin.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Lyftxxx yyyyxxxxx@lyft.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Microsoftxxx yyyyxxxxx@microsoft.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Monsantoxxx yyyyxxxxx@monsanto.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Oliver Wymanxxx yyyyxxxxx@oliverwyman.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Opendoorxxx yyyyxxxxx@opendoor.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Oraclexxx yyyyxxxxx@oracle.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Oraclexxx yyyyxxxxx@oracle.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
PayPalxxx yyyyxxxxx@paypal.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
PlaceIQxxx yyyyxxxxx@placeiq.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Remindxxx yyyyxxxxx@remindhq.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Rinsexxx yyyyxxxxx@rinse.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Riot Gamesxxx yyyyxxxxx@riotgames.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Rochexxx yyyyxxxxx@roche.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Salesforcexxx yyyyxxxxx@salesforce.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
SAPxxx yyyyxxxxx@sap.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
SAPxxx yyyyxxxxx@sap.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Sift Sciencexxx yyyyxxxxx@siftscience.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Stanley Black & Deckerxxx yyyyxxxxx@sbdinc.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
The Home Depotxxx yyyyxxxxx@homedepot.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
thredUPxxx yyyyxxxxx@thredup.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18
Thumbtackxxx yyyyxxxxx@thumbtack.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
Uberxxx yyyyxxxxx@uber.comhttps://www.linkedin.com/in/XXXXXXXHR11/19/18
UBSxxx yyyyxxxxx@ubs.comhttps://www.linkedin.com/in/XXXXXXXHM11/19/18

All Take-Home Challenges

Beside the challenges, the ebook introduction gives general advice on how to tackle data science take-home challenges. What hiring managers are looking for, how to break down the problem, what you should focus on, etc.

1. Conversion Rate

One of the most common applications of data science: look at users coming to your site, predict who is going to convert and come up with ideas to improve conversion rate!

2. Translation A/B Test

In this challenge, you will check if an A/B test has been successful. Also, you will have to find out why results look counter-intuitive and design an algorithm to avoid that problem in future!

3. Employee Retention

People data science! Can you predict when and why an employee is about to quit? This is one of the most recent, impactful and interesting applications of data science!

4. Fraudulent Activities

Any website transacting money has a team of data scientists, often called "risk team", to predict whether a transaction is fraudulent. Build the model and design a personalized user experience based on the model output.

5. Funnel Analysis

Look at how users use your site, where they click, and optimize user experience! Check click-through-rate for each page of your site and identify: what's broken, guess why and come up with ideas to improve conversion rate!

6. Pricing Test

Optimize pricing on your site by looking at a pricing A/B test results! Can you improve revenue by offering different prices to different kinds of users?

7. Marketing Email Campaign

Use Machine Learning to predict when to send a marketing email and what should be the email characteristics if you want to maximize the campaign effectiveness! Come up with ideas to personalize the email campaign so that different users get different emails!

8. Song Challenge

Very early stage startups work with json files! You'll have to parse a json and build a song recommendation model in order to increase user engagement!

9. Clustering Grocery Items

Look at user online purchases and try to automatically create categories based on what users are buying together!

10. Credit Card Transactions

Anomaly detection! Use unsupervised machine learning to identify credit card transactions that appear suspicious!

11. User Referral Program

Sites offer you a reward if you send an invite to a new user and the new user uses the product. Let's find out with this challenge the impact of a referral program!

12. Loan granting

One of the hottest fintech topics here! We have data here about when a bank grants a loan. Try to beat their model and create a more efficient way to decide who should receive a loan!

13. City Similarities


Look at user browsing behavior on a hotel travel site and cluster them based on the browsing behavior. Can you predict in an unsupervised way who is going to book a hotel?

14. Optimization of Employee Shuttle Stops

Using data to optimize the off-line world! Based on employee home address, optimize the best shuttle stop location!


15. Diversity in the Workplace

Hot topic here! Look at data about employee salaries, title, and experience and figure out if the company is treating its employees fairly or there is some discrimination going on!

16. URL Parsing Challenge

Learn how to parse a URL and suggest ways to improve a travel site search algorithm!

17. Engagement Test

Another A/B test project. Look at test results and figure out whether engagement is going up! Also, figure out when an A/B test framework might fail!

18. On-Line Video Challenge

Figure out which videos should be promoted on the home page in order to maximize revenue from ads! Can you identify the characteristics of "hot videos"? Is virality predictable??

19. Subscription Retention Rate

Companies love subscription business models! Predict subscriber churn rate and come up with ideas to improve the company revenue via personalized offers!

20. Ads Analysis

Another classical application of data science here! Can you optimize a company ads campaign? Which metric would you choose to identify the best ads?

21. Solution: Conversion Rate

An example of a possible solution for the conversion rate challenge! You can use this solution template for pretty much any challenge where you have to build a machine learning model and extract insights from the model!

22. Solution: TRANSLATION A/B TEST

Solution for the third challenge. Another example to solve challenges which are based on looking at the data, building a model and then extract info from the model!

23. Solution: Employee Retention

An other reusable template on how machine learning can be used to predict when and why an employee decides to change job!

24. Solution: FRAUDULENT ACTIVITIES

Are you applying for a fintech company, the risk team in any tech company or a bank? This solution template shows how to deal with transaction-related data!

Author

Giulio Palombo worked as a data scientist for several high-growth Silicon Valley tech companies, like Airbnb or JustAnswer.
In his career, he reviewed countless candidate take-home challenges, helped companies prepare their own take-home challenge, and coached hundreds of candidates.
All this experience and inside knowledge have been condensed on this site.

As a founder @ Data Science Bootcamps, I always recommend this site to students. Perfect to prepare for interviews and learn as well. Ike Okonkwo

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