Guide


Data Science Jobs Across 7 Industries

By Jess Feldman
Last Updated March 18, 2022

First coined by the financial industry in 2001, data science has evolved from assisting in business decisions to predicting user patterns and analyzing trends. But data science jobs are no longer limited to finance and tech. Today, data science is highly applicable to pretty much any industry – from healthcare to retail to manufacturing and music platforms and these jobs continue to evolve. Lanisa Farnsworth, Manager of Employer Relations at Lighthouse Labs, explains how data science is used across 7 different industry sectors!

Meet the Expert: Lanisa Farnsworth 

  • Lanisa Farnsworth is the East Coast Manager of Employer Relations at Lighthouse Labs.
  • Lanisa works to connect graduates with employers that are hiring for paid internships and full time positions in both the fields of Web Development (primarily in the Montreal and Ottawa area), and of course, Data Science roles (all over Eastern Canada).

Data Science 101

Data science was coined in 2001 by the financial industry, the first industry to understand the advantages of sorting, analyzing, and using large amounts of data to help companies reduce their losses. 

They typical tasks of today’s data scientists include:

  • Acquiring, cleaning, and re-organizing data for analysis
  • Analyzing datasets to identify trends and patterns that can be translated into actionable insights
  • Presenting findings to inform data-driven decisions
  • Researching and problem-solving
  • A continued interested in statistics and mathematics

Most Used Tools for Data Science

Generally, data scientists need to know:

Data Science in 7 Different Industries

Today, data science can be leveraged and capitalized on in any industry, so long as the specific challenges within that industry are understood as well as how data characteristics can match the market needs with capabilities and solutions. A data science career largely depends on the individual, their experiences, and their passions. 

Data science tasks are largely the same across industries, but how data science is used in each industry varies. Here we offer some examples of how data science is used in: banking/finance, media/advertising, healthcare, government, manufacturing, tech startups, and retail

1. Banking & Financial Industry

The industry that first understood the power of data science, banking and finance typically use data science for risk analysis and risk management. Companies are looking for data scientists for portfolio management, to analyze financial trends through data using business intelligence tools.

Typical job titles: Data Scientist, Data Analyst, Business Data Analyst, Financial Data Analyst, Data Scientist - Financial Services, Statistical Analyst / Risk Analytics

Example of data science in banking/finance:

  • Data scientists in the financial industry will be helping with fraud detection. Various mission learning tools identify unusual patterns in trading data that will alert financial institutions to investigate.  

2. Media, Advertising, & Marketing

In marketing, media, and advertising, data science is used to leverage social media and mobile content in order to understand real-time media content usage patterns from people that use that media. Employers in this industry like to hire data scientists who come from a marketing background.

Typical job titles: Data Scientist, Data Analyst, Data Scientist - Product Management, Product Manager for Marketing Analytics, Data Analyst - Digital Advertising, Marketing Science, Data Science - Customer & Marketing Analytics, Data Analyst (Growth Marketing).

Examples of data science in marketing:

  • When targeted ads start showing up on Facebook or Instagram after a user searches for something on Google, that's the back end work of data science! Data science is accumulating a user’s rhythms and search patterns, then pitching ads for what a user may be interested in. 
  • Spotify and other music platforms collect, analyze, and use data from its millions of users to be able to provide better music recommendations.

3. Healthcare

Healthcare is a major field using data science, mainly to improve health and patient care. Data science is used to get physicians the most comprehensive information on their patient's well-being and be able to provide a more actionable plan for their health.

Typical job titles: Data Scientist, Data Analyst, Clinical Data Scientist, Product Data Analyst

Examples of data science in healthcare:

  • Wearable trackers provide important information to physicians who can make use of this data to provide better care for their patients, such as if the patient is taking their medication or following the right treatment plan.
  • The healthcare industry is building smart algorithms to be able to properly analyze patients' health and produce relevant results. It’s also predicting the right methods to make healthcare more efficient.

4. Government

Big data has many applications in the public service field. Government positions can include work in financial market analysis, health-related research, environment protection, energy exploration, and fraud detection. 

Typical job titles: Business Data Analyst - Government & Public Sector, Data Scientist, Data Analyst, Government Research Data Analyst. 

Examples of data science in government:

  • Big data analytics by the Social Security Administration uses data to analyze large numbers of social disability claims that come from unstructured data. Analytics are then used to rapidly process medical information and detect fraudulent or suspicious claims.
  • The Food and Drug Administration (FDA) uses data to identify and analyze patterns related to food illnesses or diseases.

5. Manufacturing

The manufacturing industry is an overlooked industry for data science application, but they use data science to boost their production system and revenue. Manufacturing also depends on data science to detect the number of products being manufactured. 

Typical job titles: Quality Data Analyst, Systems Analyst, Machine Learning Engineer, Data Scientist, Data Analyst 

Example of data science in manufacturing:

  • Throughout the production process, manufacturers collect data to calculate efficiency and performance of production machines, and to analyze efficiency related to defects and speed of production.  

6. Tech startups

Entering a tech startup as a data scientist means wearing many hats. Tech startups use data science as a way to enable faster growth and compete within the marketplace. There is risk involved in a startup, but being able to refer to numbers can greatly mitigate those risks and ensure the proper return on investment (ROI) so the company can grow. 

Typical job titles: Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer, Data Professional, Data Science Specialist, Data Analyst - Growth, Data Support Specialist, Data Product Specialist, Data Strategist.

Examples of data science in tech startups:

  • Tech startups use data science to help their team better understand underutilized profit-making platforms.
  • Data science can uncover hidden opportunities and identify trends, patterns, and problem areas for the startup. 

7. Retail

Data science in retail is similar to media/advertising/marketing because it relies on analyzing customer trends. The difference in retail is they focus on a specific market rather than a broad spectrum of customers. Data science in retail is focused on brand specifics and customer purchasing habits to enable brand growth. 

Typical job titles: Data Scientist, Data Analyst, Analytics and Insights Manager, Analytics Lead, Business Intelligence Analyst, Merchandise Analytics, Commercial Analytics, Products Data Analyst/ Scientist.  

Example of data science in retail: 

  • Retail is focused on boosting revenue, and may use data science to understand how to engage their customers and determine which products are popular. 

Hiring Trends in Data Science

There isn’t a “best” industry sector to get started in as a data scientist. This career is highly tailored to the individual, their background, their passions, and where they can leverage their career goals within data science. At Lighthouse Labs, the industries that recruit the most data science students are healthcare, tech startups, and a mix of retail and media industries. 

Typical Data Science Salaries in Different Sectors

The national average salary in Canada for a data scientist with some experience is $110K annually. A data science salary depends on a few different factors, such as which industry a data scientist works in:

  • Financial industries tend to pay higher salaries, while nonprofits and education industries compensate on the lower end. 
  • Enterprise organizations will typically pay more than a startup. It’s not always the case, as startups sometimes have more funding, but typically, the larger the organization the higher the salary. For instance, if you’re hired by Google as a Senior Data Scientist, you could make $150K-200K a year. 

Salary also differs based on region, due to factors like the local cost of living, the current job market, and company size. 

Graduates coming out of Lighthouse Labs tend to acquire Junior data science roles. An entry-level data science position on average starts around $65K, depending on the individual and their background pre-bootcamp. Some Lighthouse Labs graduates with previous experience or higher degrees (like Master’s or Ph.Ds) are able to elevate their starting salary. Even though they are starting on a junior level with their data science experience, these graduates will see starting salaries of $80K-85K. 

Interviewing for Data Science Roles in Different Sectors

As long as you have the data science knowledge, skills, and tools required, there’s no reason why you can’t pivot into different industries as opportunities arise and interests change. Many companies are satisfied with the knowledge bootcamp grads possess, while other companies want that on top of a higher education degree. 

More niche industries like healthcare or finance do seek people with those backgrounds. Financial industries look for bachelor’s degrees in mathematics, statistics, or accounting. Healthcare industries vary in their requirements of background; if someone has a great personality without a technical health background, they’re more open to considering them. 

Becoming a Data Scientist after Lighthouse Labs

The data science curriculum at Lighthouse Labs is based on the newest trends. It’s a three-month intensive bootcamp that requires 12-hour days, 6-days a week. We ensure that bootcampers learn the fundamental and most industry-relevant tools. The tools bootcamp students learn enable them to be competitive candidates in the job market. Lighthouse Labs has a 97% success rate of placing graduates in jobs within 6 months of graduating! 

Besides the actual data science curriculum, students and graduates have access to our career advisors at Lighthouse Labs. Our Careers Services team supports bootcampers with: 

  • Soft-skills workshops
  • Resume writing
  • Updating LinkedIn profile
  • 1:1 resume reviews
  • Mock interviews

3 Favorite Resources for New Data Scientists

  1. LinkedIn is the biggest resource for data science graduates. It allows you to connect with so many professionals, employers, and communities. Within LinkedIn are community pages, such as 365 Data Science, which offers tips, short tutorials, and networking. 
  2. 164 Data Science Interview Questions is a great resource for graduates to go through to feel prepared for data science interviews. 
  3. For those seeking to upskill without paying a lot of money, resources like Coursera, Udemy, and Codecademy are all online educational websites that provide workshops and training to add on to your educational journey. For instance, if you wanted to dive deeper into Python or Tableau or visualization, you could find a workshop to add on to your skillset!

Find out more and read Lighthouse Labs reviews on Course Report. This article was produced by the Course Report team in partnership with Lighthouse Labs.

About The Author

Jess is the Content Manager for Course Report as well as a writer and poet. As a lifelong learner, Jess is passionate about education, and loves learning and sharing content about tech bootcamps. Jess received a M.F.A. in Writing from the University of New Hampshire, and now lives in Brooklyn, NY.

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