Interview Landscape & Preparation Strategy
Company Research & Preparation
Generic preparation isn't enough. Each company has unique interview patterns, products, and culture. Deep company research separates candidates who get offers from those who don't.
Understanding the Data Stack
Before any interview, know the company's technical environment:
| Research Area | Where to Find | Why It Matters |
|---|---|---|
| Data warehouse | Engineering blog, job postings | Shows SQL dialect (Snowflake, BigQuery, Redshift) |
| BI tools | Glassdoor, LinkedIn posts | Tableau, Looker, Mode - affects visualization questions |
| Experimentation platform | Tech talks, blog posts | Homegrown vs vendor (Optimizely, LaunchDarkly) |
| ML infrastructure | Engineering talks | Batch vs real-time, cloud provider |
Example research output:
Company: Airbnb
- Data warehouse: Hive/Presto (now Spark)
- BI tool: Superset (open-sourced by Airbnb)
- Experimentation: ERF (internal platform, heavy A/B focus)
- Culture: Data-informed decisions, strong experimentation
Product Knowledge Requirements
Interviewers expect you to understand their products:
For consumer products (Meta, Netflix, Spotify):
- Use the product actively for 2+ weeks before interview
- Identify 3 features you'd improve with data
- Understand monetization model (ads, subscription, etc.)
For B2B products (Salesforce, Snowflake):
- Read customer case studies
- Understand key metrics for enterprise sales
- Know competitive landscape
Pro tip: During case studies, reference actual product features. "For Airbnb, I'd look at search-to-booking conversion because..." shows product intuition.
Finding Sample Interview Questions
Build a question bank specific to each company:
| Source | Quality | Access |
|---|---|---|
| Glassdoor | Medium | Free (limited) |
| Blind | High | App required |
| DataLemur | High | Free + Premium |
| LeetCode Discuss | Medium | Free |
| LinkedIn (employees) | High | Network needed |
Research strategy:
- Search "[Company] data science interview questions [year]"
- Filter for recent experiences (last 12 months)
- Note patterns: SQL difficulty, stats topics, case study format
- Look for interviewer names on LinkedIn (optional)
Leveraging Your Network
Warm connections dramatically improve your odds:
Getting referrals:
- Search LinkedIn for 2nd-degree connections at target company
- Reach out with specific, low-friction ask:
Hi [Name], I noticed you're a data scientist at [Company].
I'm preparing for an interview there next month. Would you
have 15 minutes for a quick call about the interview process?
Happy to share any resources I've found useful in return.
Informational interviews:
- Ask about team culture, not interview tips (builds trust)
- Follow up with a thank you and any resources you promised
- Never directly ask for a referral in first conversation
The Pre-Interview Checklist
Complete this 48 hours before every interview:
- Reviewed company's last 2 earnings calls (public companies)
- Read 3 recent engineering blog posts
- Practiced 5 SQL problems in their likely dialect
- Prepared 2 thoughtful questions about the team's work
- Tested all technical setup (camera, mic, IDE sharing)
- Reviewed my resume - can speak to every bullet point
- Researched my interviewers on LinkedIn
- Prepared a 2-minute "tell me about yourself" tailored to this role
Remember: Interviewers want to like you. Genuine enthusiasm for their product and preparation shows respect for their time.
The candidate who knows why they want THIS job at THIS company always outperforms the one applying everywhere. :::