Transcript Data Scientist

CAREERS IN DATA SCIENCE –
OPPORTUNITIES & CHALLENGES
San Diego Women in Data Science
Kim Smith-Rohlfs
SoCal Tech Recruiter
KIM SMITH-ROHLFS

President, SoCal Tech Recruiter
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19 years experience as a technical recruiter
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Recruited in multiple industries - Defense, Retail, Manufacturing,
Finance, Computer Hardware, Consulting Firms, Medical Devices,
Software Development
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Currently specializing in recruiting Software Developers & Big
Data Professionals
DATA SCIENCE
Career Opportunities
Challenges
Overcoming the Challenges
INDUSTRY & PUBLIC SECTOR
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Manufacturing
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Technology
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Retail
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Education
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Advertising
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Research
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Automotive
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Government
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Finance
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Non-profits
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Healthcare
DATA SCIENCE – THE PROMISE OF
COMPETITIVE ADVANTAGES
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Ability to make better business decisions
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More efficient operations
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Greater profits - improvements in sales and marketing
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Customer acquisition & management
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Risk management
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Crime prevention/management
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Threat detection
DATA SCIENCE JOBS ARE HOT!
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Data Scientists
Data Managers
Data Architects
Data Engineers
Database Administrators
Data Analysts
Business Analysts
LOTS OF CONTRADICTIONS
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“The sexiest Job of the 21st Century” - Harvard Business
Review, October 2012
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#1 of the 25 Best Jobs in America – Glassdoor, January 2016
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“It’s Already Time to Kill the “Data Scientist” Title The Wall
Street Journal (CIO Journal), April 2014
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“Hottest job? Data scientists say they’re mostly digital
‘janitors’- Computerworld, March, 2016
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“Data science is still white hot, but nothing lasts forever” –
Fortune Magazine, May 2015
CHALLENGES TO EMPLOYMENT/CAREER
ADVANCEMENT
Lack
of clarity – lots of overlap
Evolving
Hiring
Managers looking for unicorns
Training
OVERCOMING THOSE CHALLENGES
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Know what you want to do
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Different Jobs require different skills (but there’s a lot of overlap)
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Get the right skills & experience
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Start networking
WHERE DO YOUR INTERESTS LIE?
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Data Scientist – Math, statistics, programming, problem solving
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Data Engineer –Programming skills, database knowledge, problem
solving
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Data Analyst – Statistics, Excel, problem solving
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Don’t take job descriptions too literally
DATA SCIENTIST
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A data scientist is someone who knows more about programming
that a statistician, and more statistics than a software engineer.
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A data scientist will be able to run with data science projects
from end-to end: they will store and clean large amounts of
data, explore data sets to identify potential insights, build
predictive models, and weave a story around the findings.
From “Getting Your First Data Science Job” - Springboard
DATA ARCHITECT
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Data Architect is someone who can understand all the sources of
data and work out a plan for integrating, centralizing and
maintaining all the data.
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He must be able to understand how the data relates to the
current operations and the effects that any future process
changes will have on the use of data in the organization.
By Aditya Singh, Quora
DATA ENGINEER
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Data engineers are software engineers who handle large amounts of data,
and often lay the groundwork for data scientists to do their jobs effectively.
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They are responsible for managing database systems, scaling the data
architecture to multiple servers, and writing complex queries to sift through
the data.
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They might also clean up data sets, and implement complex requests from
data scientists (e.g. they take the predictive model from the data scientist
and implement it into production-ready code).
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From “Getting Your First Data Science Job” - Springboard
DATA ANALYST
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Data analysts sift through data and provide reports and
visualizations to explain what the data can offer.
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When somebody helps people from across the company
understand specific queries with charts, they are filling the data
analyst (or business analyst) role.
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In some ways, you can think of them as junior data scientists, or
the first step on the way to a data science job.
From “Getting Your First Data Science Job” - Springboard
SKILLS YOU NEED TO GET THE JOB
Data Scientist
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No one path
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Heavy math skills – calculus to linear algebra
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Statistics
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Algorithms
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Data Visualization
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Business/domain knowledge
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Good communication skills
DATA SCIENTIST – TOOLS
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SQL
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Python
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Hadoop
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NoSQL
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R
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Database technologies
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Java
NOTE: Candidates who know R earn about $20K more than those
who know Python. Candidates who used 15 or more tools made
$30,000 more than those who used 10 -14 tools.
DATA ARCHITECT/ENGINEER - TOOLS
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Hadoop-based technologies, such as MapReduce, Hive, and
Pig
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Database technologies such as. MySQL, Cassandra, and
MongoDB, SQL, NoSQL
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Data warehousing solutions
DATA ANALYST - TOOLS
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Excel
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Querying language (SQL, Hive, Pig)
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Scripting language
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Analytic tools
LISTS OF POPULAR ONLINE COURSES
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http://www.kdnuggets.com/2015/10/best-data-science-onlinecourses.html
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http://www.learndatasci.com/best-data-science-online-courses/
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http://datascienceacademy.com/free-data-science-courses/
DO YOUR HOMEWORK
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These things are popping up like diet plans. Check them out
thoroughly so you don’t waste time & money
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Google for reviews
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Ask around on Quora, Reddit, kdnuggets & Kaggle
RESOURCES – KAGGLE.COM
 Create
a profile – let recruiters and hiring managers
find you!
 Build
 Play
a data science portfolio
with their data sets
 Write
& share codes
 Enter
competitions
 Search
job board
ADDITIONAL RESOURCES
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http://www.kdnuggets.com/
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http://www.oreilly.com/data/free/files/stratasurvey.pdf
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Job listings, industry news, courses, webcasts, tutorials
Data science salary survey
https://www.oreilly.com/learning
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Webinars, tutorials and bootcamps
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https://www.oreilly.com/topics/data-tools
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https://www.springboard.com/
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Check out the links in their Resources section
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Get their free ebook -A Beginners Guide to Getting Your First Data
Science Job http://tinyurl.com/zplx29x
QUESTIONS?
LET’S KEEP IN TOUCH!

Kim Smith-Rohlfs
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[email protected]
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Office: 760-652-5967
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Twitter: @SoCalTech1
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LinkedIn: https://www.linkedin.com/in/socaltechrecruiter