Top 9 Data Science Careers of the Future

Rashi Desai
Jul 29, 2020

Photo by Alex Knight on Unsplash

In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that data is analyzed. Data is now the frontier for business or I can say it has become fuel for industries.

There are various industries like banking, finance, manufacturing, transport, e-commerce, education, etc. that we’ve heard use data science. In this article, I have listed 9 Data Science careers that have undergone a transformation with the rise of Data Science and related fields. We will see how Data Science has revolutionized the way businesses perceive data and excite you about what is in store for the future.

There will be a Part II to this article with more Data Science Careers of the future sometime very soon. Keep following!

1. Athletics

My first introduction to analytics in athletics happened in July 2017 in a web series based on the sport cricket where a team analyst tracked a baller’s past and presented performances, zeroed down his weaknesses, identified strong points, gave pain points of the opponent and devised a strategy based on data to enhance player’s performance in succeeding matches.

Cut to 2020, coaches and teams can now view trends combining data from player’s performance, heart rate, calorie intake, blood reports, speed, acceleration, and deceleration on the field and whatnot. Data analytics can make sense of athlete data, delivering insights that enable coaches to cater to player training and improve team performance.

In athletics, the difference between winning and losing can come down to milliseconds or millimeters and data can dictate those.

There are three major tools used for Sports Analytics:

SAP Sports One

SAP Sports One software helps clubs and teams digitalize sports performance management by real-time analytics with interactive dashboards for coordinative training and team management;

Microsoft Sports Performance

Microsoft’s sports performance platform is a customized sports analytics solution that aggregates and visualizes stats with big data and machine learning algorithms to provide predictive outcomes for tracking and improving athlete and team performance.

Sports Management Analytics by Tableau

Tableau offers interactive dashboards to create routines, graphs, and charts to provide better solutions to fix pains, predict outcomes, and adequately prepare athletes with the physical and mental demands of sports. Stakeholders also use Tableau to create dashboards for the audience.

2. Aviation

Extraordinary problems require extraordinary tincture!

Given the ever-evolving times of the global pandemic, it is no hidden fact about the aviation industry bearing heavy losses around the world. Domestic or international, airlines are operating at less than 30% occupancy ratio and operating profits.

In better times, the large rise in air-fuel prices and marketing expenses for airlines take a toll on the operating profits. It was not long before when airlines recognized the potential to introduce Data Science into their strategic pipeline.

Use Cases for Aviation
  1. Improve fuel efficiency
  2. Make faster business decisions
  3. Predict optimum airline routes
  4. Predictive maintenance solutions
  5. Handle customer loyalty programs
  6. Feedback Analysis
  7. Crew Management

Oliver Wyman is a good website resource for things about tech in aviation. Per its website, advanced analytics could mean significant savings for airlines; as good as saving between 2% — 2.5 % of total global operating costs, which translates to between $5 billion and $6 billion annually!

3. Dating

How else did you think you were getting matches? 😉

It seems more likely now to meet your significant other on a dating app than in real life. Online dating sites and apps are loved for their match-making abilities. To help find perfect mates for what the users have signed up, dating apps use data science algorithms fed with enormous data stores.

Clustering and correlation

Using clustering and Pandas to find correlations among dating profiles. profiles can potentially be clustered together with other similar profiles. This will reduce the number of profiles that are not compatible with one another. From these clusters, users can find other users more like them. Dating apps heavily rely on clustering to find the top similar dating profiles to one user account.

Natural Language Processing (NLP)

Dating apps use NLP to find insights about users and assess potential compatibility with other users based on their preferences. The model uses input from multiple users to look for other users with similar input to make a match. These models are trained based on user profiles to put similar profiles in the front line.

Deep Learning (DL)

Deep Learning sorts user profiles through facial features that have been liked or swiped or not. Depending on the nature of user swipes are, the variety of options presented to you will change.

Recommender Systems

Models that identify common interest and lets you choose things to do together — movies, play, travel, etc. Such machine learning models help users figure the next best move.


Bumble, OkCupid, Tinder, Hinge, Match and more

4. Finance

Finance is all about numbers. It has always been a field that involves processing a lot of data. With the advent of Data Science, financial organizations rely on automated algorithms and complex analytical tools more than ever to get ahead of the curve.

In a money field, it is quintessential to have a guiding force for decision making on how and when to take the next step, when to pull the funds back, or when to put in more money. Data allows easy judgment of the fleeting money markets in the form of analytics, personalization, and predictions

Use Cases for Finance
  1. Risk Analytics
  2. Customer Analytics
  3. Real-time stock analytics
  4. Consumer data management
  5. Stock value prediction
  6. Investment forecasting
  7. Fraud detection (credit/debit cards)
  8. Algorithmic stock trading

5. Gaming

With more than 2 billion players all over the world (source unknown), game data is a large repository, less explored. The gaming industry is a resource of enormous revenues expected to grow further on in the near future given the current times of “stay-at-home”. With an exponentially growing number of users, the amount of data to be collected and proceeded is enlarging. Player time, interaction time, quitting point, peaks of activity, results, scores, etc. present a vast material for data analytics.

Data science in gaming is majorly utilized to build models, to analyze and identify optimization points, make predictions, and empower machine learning algorithms, identify patterns and trends improve gaming models.

Use Cases for Gaming
  1. Game Development
  2. Game Design
  3. Player Analytics
  4. Object Recognition
  5. Game Monetization
  6. Fraud Detection (payments)

For gaming, enthusiasts also seek the avenue of Business Intelligence often for strategies, decision-making, and revenue monitoring.

6. Healthcare

Healthcare is thought to be the most evolving field in terms of its reliance on data — analytics and visualization.

Insurance data is a horizon rapidly expanding its use of data. Insurance data usually includes claims and covers of costs caused by the disease, accident, disability, or death. Data science in insurance with machine learning and AI models allows insurance providers to monitor and reduce risk, improving the customer experience.

Use Cases for Insurance Data
  1. Fraud detection
  2. Claims prediction
  3. Risk assessment
  4. Personalized healthcare plans
  5. Price optimization
  6. Lifetime value prediction
  7. Virtual Assistants
  8. Bioinformatics

With data science, it is now possible to obtain accurate diagnostic measures. There are several fields in healthcare such as detection, discovery, imaging, a predictive diagnosis that make use of data science.

Use Cases for Medicine
  1. Medical image analysis
  2. Drug development
  3. Cancer detection
  4. Genetics & Genomics
  5. Behavioral analytics
  6. Insurance and claims data
  7. Telehealth
  8. Wearable data to monitor patient’s data

7. Marketing

In the rapidly expanding technological world of data, businesses are abundant with the wealth of data. Proportionately, consumers expect more personalized shopping experiences than ever. That is when marketing analytics kicks in. Marketing analytics measures, manages and analyzes marketing performance to maximize its effectiveness and optimize Return on Investment (ROI).

Understanding marketing analytics allows marketers to be more efficient at their jobs and minimize wasted web marketing dollars.

Use Cases for Marketing
  1. Sentiment Analysis
  2. Customer Segmentation
  3. Affinity Mapping
  4. Recommender Systems
  5. Pricing Strategy
  6. Predictive analytics for customers’ behavior
  7. Market basket analysis
  8. Real-Time Interaction Marketing
  9. Budget Optimization
  10. User groupings
  11. Audience Identification
  12. Targeting the right channels
  13. Lead Targeting
  14. Content Strategy Creation

Now show me a career less exciting than that in Marketing.

8. Streaming services

Over the past couple of years, online streaming has become the de-facto destination for viewers looking to binge on movies and TV shows. Streaming services are all in for user personalization. Data Science empowers with that very deliverable. Platforms get a more realistic picture of consumers’ tastes based on several inputs.

Watch these videos below on how Netflix uses its data!


Hulu, Disney, Netflix, Amazon for Prime Video, Pandora, Apple Music and TV, Spotify are heavy users and recruiters in the field of Data Science and Analytics.

Use Cases for Streaming Services
  1. Recommender Systems
  2. Customer Analytics
  3. Real-time streaming analytics
  4. Content personalization
  5. Search optimization
  6. Content Creation
  7. Customer Segmentation
  8. Affinity Mapping
  9. “Event” tracking — play, pause, quit, date of watching9. Weather

For Chicago, it’s an adage that you curse the weather for 30 minutes and it changes!

Weather is gullible. A perfectly sunny day can ruin your outing with a downpour and therefore, an accurate prediction with high confidence is essential. Weather forecasting is data about the atmosphere.

I read an article where IBM bought The Weather Company to leverage its data to feed its famous AI machine, Watson and from that very acquisition was born Deep Thunder — hyper-localized forecasts with a high degree of accuracy. Many large-sized conglomerates are heavily investing in weather and its data for the decision-making process in various horizons — farming, sports, asthma patients, car sales (Apparently, rainy days attract people to buy cars! 🚘)

Use Cases for Weather
  1. Exploratory data analysis
  2. Weather AI for agriculture
  3. Weather forecasting
  4. Satellite Imagery
  5. Image Sensing

Some time back, the east coast of India was hit by the strongest cyclone in the last 20 years named ‘Fani’. Thirteen days before the cyclone hit the area, India Meteorological Department had an indication that there could be a massive storm involved and they started preparing for the outbreak. A record 1.2 million people were evacuated in less than 48 hours just because of data scientists! Now, that’s the reason for having weather on this list!!
That’s it from my end for this blog. Thank you for reading! I hope you enjoyed the article. Do let me know what careers are you looking forward to exploring your Data Science journey?

Happy Data Tenting!

Disclaimer: The views expressed in this article are my own and do not represent a strict outlook.

Know your author

Rashi is a graduate student at the University of Illinois, Chicago. She loves to visualize data and create insightful stories. When not rushing to meet school deadlines, she adores writing about technology, UX, and more with a good cup of hot chocolate.

“Top 9 Data Science Careers of the Future”
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