Want to see the real deal?
More inside scoop? View in App
More inside scoop? View in App
blind
SUPPORT
FOLLOW US
DOWNLOAD THE APP:
FOLLOWING
Industries
Job Groups
- Software Engineering
- Product Management
- Information Technology
- Data Science & Analytics
- Management Consulting
- Hardware Engineering
- Design
- Sales
- Security
- Investment Banking & Sell Side
- Marketing
- Private Equity & Buy Side
- Corporate Finance
- Supply Chain
- Business Development
- Human Resources
- Operations
- Legal
- Admin
- Customer Service
- Communications
Return to Office
Work From Home
COVID-19
Layoffs
Investments & Money
Work Visa
Housing
Referrals
Job Openings
Startups
Office Life
Mental Health
HR Issues
Blockchain & Crypto
Fitness & Nutrition
Travel
Health Care & Insurance
Tax
Hobbies & Entertainment
Working Parents
Food & Dining
IPO
Side Jobs
Show more
SUPPORT
FOLLOW US
DOWNLOAD THE APP:
comments
Some questions they might ask on an interview:
“1) Compare and contrast the mathematical machinery used in boosting vs. bagging. How does this impact algorithm speed and accuracy?
2) Derive a likelihood ratio test based on these two models/statistical distributions.
3) Give a distance metric and ask candidate to derive a nonparametric statistical test.
4) What was the last machine learning paper you read? Critique the method and suggest potential ways to improve that algorithm (speed or accuracy).
5) Create your own neural network model using a novel mapping function.
6) How can topology be leveraged to extend statistical methods? How have TDA tools like persistent homology interacted with statistics?”
Math is a very big topic