359: Tackling Data Science Job Hunting, Interviews & Negotiations

Episode Summary

Executive Summary: Emily Robinson, senior data scientist at Warby Parker and co-author of Build a Career in Data Science, discusses practical ways to enter and grow in data science. The episode covers career fit, analytics/decision science/machine learning, R vs. Python, company archetypes, job search strategies, interviewing, and salary negotiation, emphasizing community, portfolio-building, and thoughtful negotiation.

Main Topics: What data science is and who it suits: Emily frames data science as a broad field spanning analytics, decision science, and machine learning, and encourages people from many backgrounds to enter if they enjoy working with data, communication, and problem-solving. R vs. Python for beginners: The discussion compares the two dominant languages, with Python better aligned to job-market demand and production ML, while R is praised for readability, beginner-friendliness, visualization, and community. Company archetypes and choosing the right workplace: Emily and Kirill discuss five company types—big tech, established retail, government contractor, late-stage startup, and small startup—and how factors like bureaucracy, security, and mentorship should guide job decisions. Getting interviews through portfolio and networking: Advice centers on building GitHub projects, blogging, meetups, conferences, and targeted outreach rather than mass applications, especially for candidates without prior data science job experience. Interview preparation and case studies: Emily explains that interview processes vary widely and may include SQL, whiteboard coding, statistics, behavioral interviews, or take-home case studies, and recommends preparing for each format and understanding what evaluators are seeking. Salary and offer negotiation: A major segment focuses on negotiation tactics: avoid naming a number first, wait to hear the offer, stay enthusiastic, use competing offers or current compensation as leverage, and negotiate beyond salary using bonuses and benefits. Career mindset and community: Emily stresses that career paths are personal, that gatekeeping should be ignored, and that finding community helps with skill-building, mental health, and job-search momentum.

Key Arguments: Data science is accessible to people from many educational and professional backgrounds if they can build enough statistics, programming, and domain knowledge. The field can be understood through three practical lenses: analytics, decision science, and machine learning. Python is more common in job postings and production machine learning, but R is often friendlier for first-time programmers and has a strong community. When choosing jobs, candidates should evaluate company structure, mentorship, bureaucracy, and security rather than chasing prestige alone. Building a portfolio with GitHub projects and blog posts is one of the strongest ways to demonstrate capability without prior work experience. Networking through meetups, conferences, and thoughtful cold outreach is often more effective than mass online applications. Negotiation can materially improve compensation; candidates should not reveal salary expectations too early and should negotiate the full package, not just base pay. No one needs to know everything before applying; once a solid foundation is in place, the market can help determine readiness.

Data Points: Episode number: 359 - Super Data Science Podcast episode featuring Emily Robinson Conferences/meetups spoken at: 26 - Emily’s speaking count since July 2017 as mentioned in the intro Timeframe of speaking activity: under 3 years - The episode notes Emily spoke at 26 events in under three years Course/library size of SDC membership: 2,500+ video tutorials - Promotional intro for Super Data Science membership Course/library size of SDC membership: 200+ hours of content - Promotional intro for Super Data Science membership Course/library size of SDC membership: 30+ courses - Promotional intro for Super Data Science membership Boot camp duration: 3 months - Emily describes boot camps as not taking someone from zero to fully fledged data scientist in three months A/B test example: 50 out of 100 vs. 51 out of 100 - Used to explain uncertainty and why statistics matter in decision science Potential salary increase: 5% or more - Emily says negotiation often yields at least a 5% increase Negotiation example: $10,000 increase - A person with a lower-than-current offer negotiated up by $10,000 Negotiation example: $5,000 more plus sleep options - A friend who already had a 40% raise still negotiated further Competing offers example: $200,000 offer vs. two $250,000 offers - Illustrates leverage from multiple offers in a principal data scientist negotiation Competing offers example: 5 offers - One candidate had five simultaneous offers, used as negotiation leverage Conference travel example: Netherlands, London, Spain - Emily describes speaking opportunities that arose from networking and conference visibility Discount code: 40% off - Book promotion for podcast listeners Book promo code: podsuperdatasc19 - Code mentioned for Manning purchase of Build a Career in Data Science

Pivotal Quotes: "This is your own journey, and data science will look very different for different people." — Emily Robinson: Emily’s closing advice encouraging listeners to define success on their own terms "Don't listen to the gatekeepers." — Emily Robinson: A direct rejection of narrow views about who can be a real data scientist "You're much better served to just like start with one, after experimenting with both, perhaps, then stick with one, really learn that well." — Emily Robinson: Advice on choosing between R and Python and avoiding shallow progress in both

Implications: Listeners should focus on practical entry points, community, and evidence of skill rather than credentials alone. For the industry, the episode argues for broader talent pipelines, better hiring practices, and more transparent compensation discussions.