1. Careers in data science – opportunities & challenges
San Diego Women in Data Science
Kim Smith-Rohlfs
SoCal Tech Recruiter

2. Kim Smith-Rohlfs President, SoCal Tech Recruiter
19 years experience as a technical recruiter
Recruited in multiple industries - Defense, Retail, Manufacturing, Finance, Computer Hardware, Consulting Firms, Medical Devices, Software Development
Currently specializing in recruiting Software Developers & Big Data Professionals

3. Data Science
Career Opportunities
Challenges
Overcoming the Challenges

4. Industry & public sector
Technology
Education
Research
Government
Non-profits
Manufacturing
Retail
Advertising
Automotive
Finance
Healthcare

5. Data science – The promise of competitive advantages
Ability to make better business decisions
More efficient operations
Greater profits - improvements in sales and marketing
Customer acquisition & management
Risk management
Crime prevention/management
Threat detection

6. Data science jobs are hot!
Data Scientists
Data Managers
Data Architects
Data Engineers
Database Administrators
Data Analysts
Business Analysts

7. Lots of contradictions
“The sexiest Job of the 21st Century” - Harvard Business Review, October 2012
#1 of the 25 Best Jobs in America – Glassdoor, January 2016
“It’s Already Time to Kill the “Data Scientist” Title The Wall Street Journal (CIO Journal), April 2014
“Hottest job? Data scientists say they’re mostly digital ‘janitors’- Computerworld, March, 2016
“Data science is still white hot, but nothing lasts forever” – Fortune Magazine, May 2015

8. Challenges to employment/career advancement
Lack of clarity – lots of overlap
Evolving
Hiring Managers looking for unicorns
Training

9. overcoming those challenges
Know what you want to do
Different Jobs require different skills (but there’s a lot of overlap)
Get the right skills & experience
Start networking

10. Where do your interests lie?
Data Scientist – Math, statistics, programming, problem solving
Data Engineer –Programming skills, database knowledge, problem solving
Data Analyst – Statistics, Excel, problem solving
Don’t take job descriptions too literally

11. Data scientist
A data scientist is someone who knows more about programming that a statistician, and more statistics than a software engineer.
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

12. Data Architect
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.
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

13. Data engineer
Data engineers are software engineers who handle large amounts of data, and often lay the groundwork for data scientists to do their jobs effectively.
They are responsible for managing database systems, scaling the data architecture to multiple servers, and writing complex queries to sift through the data.
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).
From “Getting Your First Data Science Job” - Springboard

14. Data analyst
Data analysts sift through data and provide reports and visualizations to explain what the data can offer.
When somebody helps people from across the company understand specific queries with charts, they are filling the data analyst (or business analyst) role.
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

15. Skills you need to get the job
Data Scientist
No one path
Heavy math skills – calculus to linear algebra
Statistics
Algorithms
Data Visualization
Business/domain knowledge
Good communication skills

16. Data Scientist – tools SQL Python Hadoop NoSQL R Database technologies
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 tools.

17. Data architect/engineer - Tools
Hadoop-based technologies, such as MapReduce, Hive, and Pig
Database technologies such as. MySQL, Cassandra, and MongoDB, SQL, NoSQL
Data warehousing solutions

18. Data analyst - tools Excel Querying language (SQL, Hive, Pig)
Scripting language
Analytic tools

19. Lists of Popular Online courses
courses.html

20. Do your homework
These things are popping up like diet plans. Check them out thoroughly so you don’t waste time & money
Google for reviews
Ask around on Quora, Reddit, kdnuggets & Kaggle

21. Resources – Kaggle.com
Create a profile – let recruiters and hiring managers find you!
Build a data science portfolio
Play with their data sets
Write & share codes
Enter competitions
Search job board

22. Additional Resources http://www.kdnuggets.com/
Job listings, industry news, courses, webcasts, tutorials
Data science salary survey
Webinars, tutorials and bootcamps
Check out the links in their Resources section
Get their free ebook -A Beginners Guide to Getting Your First Data Science Job

23. Questions?

24. Let’s keep in touch! Kim Smith-Rohlfs kim@socaltechrecruiter.com
Office:
LinkedIn: