Data science has always been a highly sought-after field by both job-seekers and hiring managers. In 2019, LinkedIn ranked data science as the most promising career of the year, based on salary projections and a 56% increase in job openings from 2018.
But how has the COVID-19 pandemic impacted data science roles in the United States?
According to a recent July Burtch Works survey, the global pandemic has not significantly affected data science opportunities or salaries. What’s more, it seems data science opportunities could actually be growing: according to the survey, 50% of analytics and data science organizations have either suffered no impacts (42.1%) or have actually grown in size (7.6%).
There are three reasons why data science jobs continue to grow compared to other industries:
- Becoming a more data-driven organization has proven ROI for organizations investing in analytics and can help companies cut costs
- Data science jobs can be easily done from home
- Data science is particularly valuable in these economic conditions. Many companies are enlisting data scientists to track pandemic-related behaviors, forecast market trends, and build new models and simulations to understand opportunities for recovery
So, whether you’re needing to job-search for the first time in data science or looking to change firms, here are a few ways to land a data science job during COVID-19.
Find the right data science job for you
So many different people interact with data, and it’s important to make sure you’re applying to the right role! The role of a data scientist can look very different depending on the size and stage of the company. There’s a big difference between data scientist and data analyst jobs, for example, or between data scientists, data engineers and machine learning engineers.
Data scientist positions at top tech companies allow for more specialization in a niche area and can also provide a great training ground, as many hire for internships and new grad positions.
For people looking for stability and to grow their skillset in a larger pond, large companies like Pinterest, Lyft, Uber, and Facebook offer the highest salaries in the industry, as well as strong benefits packages and investment in training and education.
Smaller startups provide a high level of ownership over several initiatives and may allow you to build a lot of the infrastructure for scalable experimentation and data analysis, such as dashboards to measure important metrics. If you value creativity and innovation and have an appetite for a high risk/reward tradeoff, check out these top-ranked data science and machine learning startups.
Freelancing in the data science field can provide a lot of freedom and autonomy, with the ability to set your own rates, schedule, and terms. If you enjoy independence and prefer running your own business, you can use popular platforms and job boards like Upwork, Fiverr, TopTal, Coding Ninjas, and Angellist to get data science jobs and build your portfolio of projects. It may take a while to receive a steady flow of freelance contracts and gigs, so many data scientists start on a more traditional path and take on side jobs before moving to freelancing full-time.
Improve your data skills through online courses
Candidates will often be rejected at the onset of the job interview process because of failure to meet job requirements. Although many data science jobs prefer a bachelor’s degree or Master’s degree in computer science, applied mathematics, statistics, economics, or related technical fields, it is easy to affordably make career pivots during social distancing with online courses and certification programs.
While each company has its own requirements, here are some valuable skills all data science job applicants should seek to possess:
- Programming skills. Python is the most popular data science language, but the programming languages R, C++, and Java/Hadoop are also frequently used. Some jobs require experience in a scripting language, like PHP or Perl.
- Advanced math skills (statistics, calculus, linear algebra). Statistical data analysis often involves experience with linear models, multivariate analysis, stochastic models, sampling methods, applied optimization, and Marketplace, Bayesian, or forecasting models. Having a strong quantitative bent will definitely help in closing knowledge gaps on the job.
- Data skills. Data scientists need to master data management (collecting and cleaning datasets), data manipulation, and data visualization. SQL helps to manage relationships between large data sets, while Tableau, Power BI, and other tools can help present data to other stakeholders in an easy-to-understand format.
- Machine learning skills. When working with big data, you may be required to use both supervised and unsupervised algorithms and reinforcement learning. Machine learning is a more advanced field of data science, but if you are interested in this specialization, this Springboard article goes over some key skills and theoretical knowledge to get a machine learning job.
- Business skills and soft skills. Since data scientists are dealing with core problems around how to improve efficiency, revenue, and user experience, data scientists need a deep understanding of how the business works, in addition to critical thinking, communication, and teamwork skills.
Craft your online presence
One of the most significant changes during COVID-19 is how employers hire. Recruiters are turning all their attention to virtual employer branding, outreach, and assessment tools.
Here are some recommendations for getting a data science job through online searches:
- Update your LinkedIn profile. Recruiters are actively using LinkedIn as a search and prospecting tool to find promising candidates during COVID-19. A 10-week study showed that recruiters spend a fifth of their time on LinkedIn profile photos, so first impressions definitely matter. Choose a professional image, create a summary section optimized for SEO keywords, and make sure past work experience is clearly presented and up-to-date.
- Update your resume and any online portfolios or personal websites. To craft a compelling data science resume, remember that recruiters are looking for a first impression. Do your industry research, identify important keywords in job descriptions, choose a visually appealing format, list out experience with various technical skills, and make sure to quantify your impact. Including a Github portfolio and showing that you’ve contributed and collaborated on a variety of projects can also help you stand out.
- Brush up on virtual interviewing skills. Companies have quickly adopted virtual interviewing tools in place of in-person final rounds. To succeed at interviewing through Zoom, Google Hangouts, Blue Jeans, or other platforms, the Society of Human Resource Managers (SHRM) recommends double-checking your tech (camera, microphone, and Internet speed), choosing a quiet, well-lit room, and making sure you’ve properly adjusted your calendar for different time zones. Set up practice sessions, so that your personality shines through in the behavior/fit portion of the interview. Leetcoding, reviewing sample questions, and reading relevant books can also help you ace the technical portion. Finally, here are some tips to ace a video job interview.
Negotiate and close the offer
Once you’ve landed one or more data science offers, the fun begins and you have the leverage to seal the deal.
- Always assume the salary is negotiable. It’s commonly said that you never know until you ask! Most employers expect pushback on their initial offer, so set a high starting salary, but also know your Best Alternative to a Negotiated Agreement (BATNA)—or the final offer you are willing to settle for. Blind has some valuable negotiation tips from tech professionals.
- Leverage competing data science offers. Applying to multiple companies can definitely increase your options and also give you another benchmark to determine how much to ask for. Most large tech companies will try to match offers at their competitors.
- Consider the entire package. During a pandemic, many people’s values shifted, with 81% saying that company culture is very important. Does the company offer flexible hours? Will remote work be allowed longer-term? Many startups offer equity and larger companies include stock options, so it’s important to understand the terms and conditions of the types of shares you are awarded, as well as the differences between cash and non-liquid compensation.
COVID-19 presents unique opportunities for remote data science jobs (both freelancing and permanent work-from-home options), opening up a larger job pool for qualified candidates. By taking advantage of this time to pivot into data science or refine your data science skillset, you can succeed in a high-demand profession and take advantage of a job market that continues to thrive in the midst of a global pandemic.
Springboard’s online mentor-driven Data Science Career Track may be the right launchpad to embark on this exciting career journey.
Ever wonder what a data scientist really does? Check out Springboard’s comprehensive guide to data science. We’ll teach you everything you need to know about becoming a data scientist, from what to study to essential skills, salary guide, and more!