This document discusses what hiring managers look for in data applicants. It begins by introducing the author and their background. It then discusses that executives want business insights, while students see data science as statistics and machine learning. Data analytics focuses on insights to improve business decisions using scientific methods and business sense, while data science also builds predictive models and data products. Effective data analysts provide relevant, reliable, and timely insights to accelerate feedback and decisions. They do deep dives, instrument KPIs, design experiments, and support various functions. Hiring assessments include phone screens for skills and communication, technical screens for skills and problem solving, and on-site interviews for scientific methods, skills, business sense, and soft skills.

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18 APRIL 2019
RUBEN KOGEL
What Hiring Managers Look for
in Data Applicants

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→Head of Data Analytics at Lime
→previously built the data practice at VSCO
and led Content Analytics at Udemy
a bit about myself

3. What *is* Data Science?
→Execs want “insights”
→Orgs need business / product / marketing
analytics, experimentation, predictive
modeling, data products
→Students think it’s mostly about advanced
statistical techniques and machine learning
→Practitioners think it’s mostly about
counting things properly

4. Different Data Functions
→Data Analytics
• serves internal customers
• insights on what’s driving the business
and how to improve it (decision support)
• requires strong product and business
sense + scientific methods
→Data Science
• serves internal and external customers
• predictive modeling and data products
• requires strong scientific methods + data
eng skills
vsco.co/evanhundelt

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→Provide data insights on how the business is doing and how to improve it
• Deep dives, KPI instrumentation, experiment design & analysis, ad-hoc support, self-
serving tools
→Insights should be relevant, reliable, and timely
→Goal: accelerate the feedback loop & drive better decisions, faster
Data Analytics Mission

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→Deep dive to gain a deep understanding of complex issues & provide
recommendations
→Design instrumentation to track progress against key business objectives
→Build reference tables and dashboards to automate data insights
→Experimental design & analysis to accelerate the learning
→Ad-hoc support to various functions - should be minimized
Typical Data Analytics Projects

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Technical Skills
• Coding skills - SQL + data manipulation language
• Basic statistical techniques & scientific methods (incl. experimental design & analysis)
• Familiar with data pipelines and data warehouse designs
Analytical Skills / Problem Solving
• Attention to details
• Structured & rigorous thinking
Product & Business sense
Soft skills
• Strong cross-functional & communication skills
• Curious, persistent, humble, and self-driven
What Data Analysts Need to Succeed

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→Phone screen
• communication, structured thinking, technical skills, product & business sense
→Technical Screens (incl. take-home if applicable)
• technical skills, analytical skills, product & business sense, communication
→On-site
• scientific methods (e.g. experimental design & analysis), analytical skills, product &
business sense, soft skills
How do Hiring Managers Assess those Skills?

9. Questions?