You’re ready for your first data science job, or maybe you’re looking to make a change of field. Data science is still the hottest job market there is, even with the field niching down. If you need some inspiration before sending out your next resume, here are fields desperate to begin hiring data scientists for 2020.
Online retail is one of the biggest things being transformed by the field of data science. Whether you’re a data scientist, data engineer, in AI or some other niche, there’s a spot in retail for you.
Right now, retail is transforming the entire way we shop. Between things like deep learning-based recommendation engines from Amazon and Netflix to the visual search employed by giants like Pinterest, there’s lots of space in both large, established organizations and smaller startups changing the retail game.
One of the most significant breakthroughs is the use of visual cues and deep tagging to transform search. E-commerce sites are finding that customized customer experiences help ensure that consumers come back. Frustrating search results, in which poor tagging and irrelevant results fail to produce efficient searching patterns.
Companies like Donde Search are revamping the way we do even our essential shopping online, but that’s not the only thing changing. Companies are also getting smarter about the product pipeline. With analytics, companies are managing inventory, ensuring that production isn’t hampered by unforeseen downtime.
Companies are also experimenting with a better tracking system for the lifecycle of products in general. Walmart China, for example, has launched an initiative that allows consumers to scan the product they’ve bought and find out where it began, how it was made, and how it traveled to their store.
Technically, humans have been bio technicians since the early days of animal breeding or crop selection. Now, we’re using our understanding of data science to accelerate our discoveries and reduce the overall cost of experimentation.
If you’re already in a scientific field, this could be the perfect career change. Biotech needs the expertise of discipline level experts to help craft experiments, read results, and provide the machines with the context required to learn the world of our smallest particles.
Data scientists are using their expertise to craft smarter models and run experiments for materials, drugs, new reactive properties, and a whole host of other things. On the other side, scientists are providing expertise for data training and understanding circumstantial information.
One substantial potential breakthrough is customized medicine and therapies that coordinate directly with our DNA. We’re working on making better predictions for genetic diseases with fewer false positives. We could also make better protocols through targeted medicine, lifestyle changes, and therapies—all made through better understandings of individual genetic code.
These predictive models are reducing negative patient outcomes and are helping pharmaceutical companies reduce adverse effects from medications and therapies that later prove more dangerous than helpful.
Healthcare is going through a revolution thanks to the surge in AI-driven data processing and NLP building human-like understanding. Right now, huge advances in processing the massive unstructured data healthcare produces — in the form of doctor’s notes, images, and patient communications — are allowing doctors better support for everything from diagnosing to treating to following up.
These big changes are only possible through deep learning and contextual NLP libraries, allowing a blend of doctor expertise with AI-driven pattern recognition. Healthcare is one of the biggest fields for startups at the moment, and the industry is ready for end to end adoption, with everyone from hospital administrators to doctors to patience getting on board.
The need for data scientists to work in this complex field is enormous. Critical expertise in compliance, working with incomplete or sensitive data sets and the ability to build theoretical examples for training sets is going to be key pieces of this field’s development.
In fact, ODSC’s AI Career Lab and Expo, part of ODSC East 2020, is hosting partners in the healthcare field eager to showcase how these real-life examples are changing the landscape of healthcare as we know it — possibly the biggest change we’ve seen since the standardization of medicine.
The finance industry is also having a bit of a revolution in a variety of ways. The fundamental way we’re doing business is changing — decentralizing in the form of blockchain both in currency and in security. Plus, as the finance industry moves forward, AI is helping traditional finance companies with security, identity verification, and lending practices.
This is probably one of the most interesting ways that the finance industry is using AI. A few years back when micro-loans changed the landscape of lending, it opened the field both to those not normally serviced by loans and to those who could lend directly, no institution required. Now, AI is helping the finance industry better evaluate a new generation of applicants that don’t conform to the traditional picture of credit.
ODSC is also featuring some groundbreaking talks from the finance industry as well as hearing from partners at the AI Career Lab and Expo. If you’re interested in how data science is turning finance on its head, you cannot miss this year’s conference. Cannot.
Data analysts have been in marketing forever, but data scientists are a relatively new development. Analysts use both internal and external datasets to build new marketing initiatives. They produce descriptive insights based on that data, and a big part of their role is monitoring their data.
Data scientists focus on improving the organizational effectiveness of the marketing initiative—think rebuilding the pipeline. They use advanced predictive modeling methods to improve this marketing pipeline and provide deeply informative insights about customer behavior.
Data scientists need the soft skills to provide these data insights because they go far beyond the numbers. Management may not be able to understand the nuanced difference between what the data scientist is creating and the business analytics of classic marketing analysts.
For example, data scientists recognized the decline of email open rates and created push notifications to get vital, real-time information out there. Data scientists also recognized the sheer frustration of visual search within eCommerce and invented ways for consumers to search online closer to the way they would search in a physical store.
These initiatives go far beyond just reading numbers to help companies define customer behavior for better personalization and to revolutionize the way consumers interact with companies. They’re providing real-time insights so that businesses can pivot immediately instead of waiting a quarter or months or even years.
They’re also identifying trends before they’re trends, giving companies a chance to jump on things that are still on the horizon instead of being a constant tick behind the curve. This prediction capability is allowing companies to survive and thrive in the fast-paced, noisy world of internet marketing to savvy, jaded online consumers.
Building a Data Science Career in 2020
While these aren’t the only fields hiring data scientists for 2020, they certainly are promising. Paying attention to the ways that companies are finding new ways to interact with data could be just the ticket to jumpstarting your career in data science for 2020, even when the data analytics has been around for a while. Use that data to revolutionize business practice, and you’ll prove invaluable.