Data Scientist as a Career
Description of the Job
Data Scientists work closely with large data, using their skills to gather, analyze, and generate conclusions about data sets. They then must interpret and model their results to assist other organizations in utilizing their results. A successful Data Scientist will constantly be required to use computer science, statistics, and mathematics in their everyday work.
Fast Facts
- Number of hours per weekGenerally 40-50 hours, but could be 50-60
- Average starting pay$92k Nationally, $110k in the Bay Area
- Amount of travel requiredGenerally no travel
Roles & Responsibilities
- Collect and polish large amounts of data
- Analyze collected data to identify trends and patterns in data
- Build predictive models, as well as AI algorithms
- Present findings using visualization tools, such as graphs or reports
- Collaborate with engineering and product development teams, amongst others
Skills Needed
- Strong Computer Science Knowledge: Data Scientists need programming skills for almost all aspects of their job
- Strong Math and Statistics Knowledge: Data Scientists will be expected to understand various key statistical and mathematical approaches. These will also aid in the creation of machine learning algorithms
- Critical Thinking Skills: Data Scientists must be able to understand the meaning behind the data they collect. Having critical thinking skills will enable Data Scientists to recognize and define trends and crucial data
- Basic Presentation Skills: Data Scientists are expected to share their analyses, and therefore should have basic presentation skills
- Moderate Communication Skills: Data Scientists work closely with members of other departments and must be able to convey their research to members of those departments. Miscommunications or misunderstandings may be costly, so they must be able to articulate their findings properly
- Strong Attention to Detail: Data is exact, and one mistake could disrupt the data set. Data Scientists should notice small details, which allow them to take every variable into account and catch any errors
- Basic Machine Learning Knowledge: While having a mastery of Machine Learning will likely not be required, a level of familiarity may be expected
Steps to Enter The Field
- A bachelor's degree is required to become a Data Scientist. Some common majors include computer science, IT, math, or a similarly related field
- Master’s degrees and PhDs are common degrees to have in the Data Science field. According to GetEducated, 73% of data scientists have a graduate degree and 38% have a PhD
- Some good programs for either a master’s or PhD are data science, computer science, or a related field
- Entry level data science internships are also highly recommended for prospective Data Scientists. It may also be useful to gain experience in the intended field (examples could be healthcare, business, or technology)
- Some universities may have data science clubs or programs for interested students
- Building a portfolio of small projects can be a great way to stand out for those trying to be Data Scientists. These projects should utilize data collection or skills specifically related to data science. Creating a blog or website that displays these projects is also useful
- It may also be beneficial to receive a certification, such as a CAP or CCP. A list of other respected certifications can be found here
Landscape of the Field & Companies in the Field
- Top players in the industry include large technology companies, such as IBM, Oracle, Amazon, Google and Microsoft.
- Other major players in the industry include Xplenty, Indium Software, Sumatosoft, and iTechArt.
- Many companies have Data Scientists in their IT department. Even companies that do not specialize in the collection of large data may still need data analysts. Banks, healthcare providers, and government agencies are examples of companies that need Data Scientists.
External Resources to Learn More & Develop Skills
- Codecademy: an online interactive platform that offers free coding classes in 12 different programming languages including Python, Java, Go, JavaScript, Ruby, SQL, C++, C#, Swift, and Sass, as well as markup languages HTML and CSS
- Simplilearn: A website containing a plethora of available opportunities for prospective Data Analysts. Some examples include post-graduate programs, master’s courses, and certification courses.
- Meetup: A website that displays various groups related to Data Science. There are communities all over the world, all interested in sharing their stories and knowledge. It also contains groups for other similarly related topics, such as machine learning, data visualization, and data science.
- Data Science Central: An online blog covering various topics, ranging from big data to data visualization to job opportunities. Experts from the data science industry contribute their stories and advice on how to succeed in the field.
- No Free Hunch: The blog of the data science website, Kaggle. They often host data science competitions, as well as providing various projects. Winners of competitions are occasionally offered interview opportunities through Kaggle, where they ask them to detail their processes.
Related Careers
- Data Analyst, Machine Learning Engineer, Logistics Analyst, Business Intelligence Analyst, Business Systems Analyst, Statistician, Data Engineer
Informational Interviews
Jimmy Nguyen
Company Name: LinkedIn
Title: Senior Data Scientist
Years of Experience: 9
Biography
Jimmy Nguyen is a Senior Data Scientist at LinkedIn. He graduated from Santa Clara University in 2012 with an undergraduate degree in Mathematics and Finance. He also received his Master's degree in Statistics from San Jose State University in 2020. Prior to LinkedIn, he worked for Symantec as a Business Intelligence Analyst. He joined LinkedIn in 2015 as a Senior Data Analyst, then became a Data Scientist in 2017. He was promoted to a Senior Data Scientist in 2020.
Overview
Steps to Enter Field
Challenges & Favorite Parts
Advice
Alejandra Budar
Company Name: LinkedIn
Title: Associate Data Scientist
Years of Experience: 4
Biography
Alejandra Budar is an Associate Data Scientist at LinkedIn. She graduated from Santa Clara University in 2017 with a major in Public Health Science and minor in French. She received her Master's degree in Big Data Solutions in 2020 from Barcelona Technology School. Prior to joining LinkedIn, she was a Data Science intern at Devex and a Data Scientist at Fields Data. She joined LinkedIn in April 2021.