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:

1. Search "[Company] data science interview questions [year]"
2. Filter for recent experiences (last 12 months)
3. Note patterns: SQL difficulty, stats topics, case study format
4. 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. :::