Photo of how to apply for the right job to kickstart your data science career

How to apply for the right job to kickstart your data science career

Hi, I’m Olga, a former lead data scientist at eBay and now advisor at Pathrise. I work with candidates who are looking to land great jobs in data science.

When you are getting ready to start applying for jobs in data science, you will find that just typing “data science jobs” into Google or LinkedIn brings up a lot of results with a lot of different titles. While they might look similar in name, they are often very different in the requirements in background and in the tasks that accompany the position.

We wanted to break down these roles so that you can get a better sense of what is right for you. Narrowing down your search to specific types of positions will help make your job search more efficient and effective.

1. Data analyst

Background:

The most common background for a data analyst is a bachelor’s degree in computer science, information technology, statistics, business analytics, economics, or a similar topic. Data analysts can also participate in a bootcamp or online degree program in order to gain the background necessary for the role.

Responsibilities:

  • Collect data on a certain topic or project and then organize, interpret, and create reports to present findings
  • Perform competitive analysis and identify industry trends
  • Use tools, systems, and datasets to discover better decisions for the business
  • Identify new sources and methods to improve data collection

Tools:

  • SQL
  • XML
  • Javascript
  • R
  • Python
  • SAS
  • Hadoop
  • Machine learning programs
  • Data visualization tools, like Tableau

2. Data scientist

Background:

Data scientists generally have a bit more experience and a stronger background than data analysts. They should be proficient in the most commonly used data science programming languages (Python, Java, and R) as well as advanced in math and statistics. Studying data science, statistics, computer science, or math in college (or in a bootcamp) as well as continuing to learn programming languages and database architectures are helpful for aspiring data scientists. Many data scientists also go onto graduate school to obtain a Master’s or Ph.d in data science or in statistics, economics, math, or computer science. 

Responsibilities:

  • Use descriptive, predictive, inferential, and causal models to explore and anticipate problems, then model a solution based on multiple factors.
  • Solve complex business problems using mathematical and algorithmic techniques.
  • Make use of the collected data, build and test statistical models, and create reports that include easily understandable data visualizations.
  • Constantly evolve and optimize.
  • Identify patterns and trends in data, then provide a plan to implement improvements.
  • Make use of predictive analytics to anticipate future demands, events, etc.
  • Research and/or invent new algorithms to solve problems and build analytical tools.
  • Recommend cost-effective changes to existing procedures and strategies.

Tools:

  • SAS
  • SPSS
  • SQL/NoSQL databases
  • MATLAB 
  • Python
  • R
  • Java
  • Hadoop platform

3. Data engineer

Background:

Data engineers typically study software engineering, computer science, information technology, or potentially another type of engineering. Courses, sometimes outside the major, in software design, computer programming, data architecture, data structures, and database management are important to this role as well. 

To break into the industry, candidates can look for IT assistant or even IT management roles before data engineer roles. Grad school is not as necessary for these roles, but there are a variety of certificates that one can obtain. For example, the Certified Data Management Professional (CDMP) credential from the Institute for Certified Computing Professionals (ICCP). Other certifications include IBM Certified Data Engineer in big data, Google’s Certified Professional in data engineering, the Microsoft Certified Solutions Expert credential in data management and analytics, and the CCP Data Engineer from Cloudera.

Responsibilities:

  • Build and maintain data pipelines, creating warehouses for big data so that are accessible.
  • Develop, construct, maintain, and test architecture, including databases and large-scale processing systems.
  • Collaborate with data architects, data analysts, and data scientists to help those team members tell the story of the data.
  • Solve complex problems on a coding level, using many different scripting languages and understanding the nuances and benefits of each.
  • Research and discover new methods to acquire data and new applications for existing data.
  • Statistical modeling and regression analysis

Tools:

  • R
  • SAS
  • Python
  • C/C++
  • Ruby Perl
  • Java
  • MatLab
  • SQL
  • Cassandra
  • Bigtable
  • Hadoop-based analytics, such as HBase, Hive, Pig, and MapReduce
  • UNIX
  • Linux
  • Solaris
  • AForge.NET

4. Business analyst

Background:

The business analyst is the connection between the data team and the business team, so the background for this role would be a degree in business administration, finance, or accounting, while also learning some programming. Many aspiring business analysts work at an entry level role in accounting, workflow, or billing before pursuing a Master’s degree or advanced certificate in business analytics, where they take courses in business data analytics, operations research, project management, database analytics, and predictive analytics.

Responsibilities:

  • Evaluate current systems within the business and develop strategic plans.
  • Using data-backed solutions, introduce change into the organization, which might include cost-cutting, identifying new opportunities, realizing and creating new benefits, etc.
  • Provide requirements to the IT department to produce new technological systems and support the testing and implementation.
  • Review and analyze datasets to increase efficiency within the company through a mix of strategic planning, business model analysis, process design, and systems analysis.
  • Communicate with colleagues, senior management, and external stakeholders the needs of the business, the changes recommended, and the processes necessary.

Tools:

  • Diagramming programs
  • Data analysis programs
  • Hadoop platform
  • SQL/NoSQL databases

When applying to data science roles, take your background and future goals into consideration so that you can make the right choice and expedite your job search.

Pathrise is a career accelerator that works with students and professionals 1-on-1 so they can land their dream job in tech. 

If you want to work with any of our advisors to get help with your data science applications or with any other aspect of the job search, become a Pathrise fellow. 

Apply today.

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