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 60-minute round structured into three 20-minute rotations. Each participant will have one rotation as the interviewee, and act as an interviewer in the other two rotations.
Interviewer (20 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 (20 min): Respond to technical questions while articulating your problem-solving approach
15 of the 20 minutes are for the interviewee's problem-solving and interviewer's questioning, and 5 min for feedback.
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
UPDATED Schedule:
5:15 - 5:30 PM: Greetings & Networking
5:30 - 6:30 PM: Round 1 - Technical Interview Practice (45 min)
6:30 - 6:35 PM: Break
6:35 - 7:35 PM: Round 2 - Technical Interview Practice (45 min)
7:35 - 7:45 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.