WiDS: Technical Interview Prep Session

WiDS: Technical Interview Prep Session

Join us for an engaging VIRTUAL practice session dedicated to sharpening your technical interview skills. This is a fantastic opportunity to practice in a supportive environment and receive constructive feedback from peers.

NOTE: This event requires active participation, so we ask all attendees come prepared to have cameras and microphones on during the practice rounds.

This session is specifically designed for Data Analysts, Data Scientists, Data Engineers, Machine Learning Engineers, and similar roles preparing for technical interviews in data-related fields.

What to Expect:
Participants will be organized into groups of three, with each 45-minute round structured into three 15-minute rotations. Each participant will have one rotation as the interviewee, and act as an interviewer in the other two rotations.

• ​Interviewer (15 min x 2): Listen to the other participants answer their selected question, provide hints if required, and deliver constructive feedback using provided rubrics and solution notes
• ​Interviewee (15 min): Respond to technical questions while articulating your problem-solving approach

Each participant will have a turn as interviewee in Round 1, then repeat the process with new questions in Round 2. No prior interviewer experience needed! We will provide answer rubric, solutions and guideline for follow up questions.

A collaborative coding platform will be provided with support for Python, Java, JavaScript, C++, R, and SQL. Participants may select coding questions at their preferred difficulty level (Easy, Medium, or Hard) or opt for non-coding technical DS/ML questions. Answer rubrics and follow-up question guidelines will be provided to facilitate structured, meaningful feedback.

Topics Covered:

• Coding: Data structures & algorithms
• Non-Coding: Statistics, ML fundamentals, A/B testing, experimentation, metrics design

Schedule:

• 5:15 - 5:30 PM: Greetings & Networking
• 5:30 - 6:15 PM: Round 1 - Technical Interview Practice (45 min)
• 6:15 - 6:30 PM: Break
• 6:30 - 7:15 PM: Round 2 - Technical Interview Practice (45 min)
• 7:15 - 7:30 PM: Networking & Farewells

How to Prepare:

• Review fundamentals: Brush up on core concepts in your chosen track (coding or non-coding DS/ML topics)
• Practice explaining your thought process: Focus on articulating your reasoning clearly and logically
• Test your setup: Ensure your camera, microphone, and internet connection are working properly
• Choose your difficulty level: Decide whether you'll focus on Easy, Medium, or Hard coding questions, or non-coding technical questions
• Be ready to give and receive feedback: Approach the session with an open, growth-oriented mindset

Preparation Resources:
Statistics & ML Fundamentals:

• StatQuest with Josh Starmer - Excellent video explanations of statistical concepts and ML algorithms
• Machine Learning Interview Guide - Comprehensive GitHub repo covering ML concepts, case studies, and interview questions
• Introduction to Statistical Learning - Free textbook with R/Python labs

A/B Testing & Experimentation:

• Trustworthy Online Controlled Experiments - Industry-standard resource on A/B testing
• Evan Miller's A/B Testing Resources - Statistical foundations and practical tools
• Netflix Tech Blog - Experimentation - Real-world examples from industry leaders

Coding Practice:

• LeetCode - Practice problems organized by topic and difficulty
• HackerRank - Statistics and data science focused tracks
• StrataScratch - Data science and SQL interview questions from real companies

General Interview Prep:

• Chip Huyen's ML Interviews Book - Comprehensive guide to ML system design and interviews
• Data Science Interview Questions - Curated list of DS interview questions and resources
• Practice Questions by Company - Organized list of DS/ML questions by company
• ​Interview Query - Technical & Non-Technical questions by company

WiDS Puget Sound is independently organized by Diversity in Data Science to be part of the mission to increase participation of women in data science and to feature outstanding women doing outstanding work.

Register here on Luma: https://luma.com/pam0gs7d