Posted by Vivian Zhang
Updated: Apr 30, 2021
Although data science is still an emerging field, the demand for qualified data scientists has skyrocketed over the last decade.
Nearly every major company manages its own data science department for tasks including research and development, business operations, marketing, sales, user experience, and more. Meanwhile, companies like Facebook and Google lead the way in machine learning and cutting-edge technologies.
You can’t visit any job board website today without running into listings for various data science roles. That’s why the BLS expects jobs in operations research like data science to grow by 25% over the next decade.
None of this means, however, that landing a fantastic job as a data scientist is easy. Even entry-level data science jobs are often hidden away from public job boards and sourced through professional networks.
Below you’ll find tips and strategies on how to get a job in data science:
Despite the explosion in data scientist jobs at companies in every industry, securing a legitimate job is another story – but why? Data scientist roles require a unique set of expert skills and companies can’t risk high turnover rates for the positions.
A few reasons why it is so hard to find jobs in data science include:
High Salaries for Data Scientist Roles Means Higher Risk
Data scientist roles pay well because they’re often responsible for overall company growth. Companies can’t afford high turnover rates in data science positions and the risk that comes with it.
Too Many Unqualified or Fraudulent Applicants
More people receive PhDs today than ever before: over 55k per year. With fewer academic jobs available as well, many PhDs have looked to data science for jobs, but a doctorate alone doesn’t necessarily prepare someone for the role’s demands.
Meanwhile, others see the salary attached to many data science roles and apply for positions without the right qualifications. Some applicants are even outright fraudulent.
Roles Require Specific Skill Sets
For specific roles that require niche skills, companies may not see public postings worth the hassle. Some companies may not even have the knowledge to vet applicants. Instead, they seek data scientists to fill the roles from trustworthy sources like data science bootcamps.
Potential Risk of Exposing Trade Secrets
Top data science jobs at places like Facebook and Google work on teams creating the most innovative technology on the planet.
These companies are also in competition with one another to develop machine learning, NLP, and Big Data technology. They can’t risk revealing trade secrets should the job not work out.
Data scientists have advanced experience in both programming and data analysis. Less-than-reputable companies sometimes use the “data scientist” title in job postings to attract applicants while the role is really for a basic analyst.
On the other hand, some “data analyst” job post titles actually reflect data scientist roles in the description. Learning how to reach a data scientist job description is one of the best things you can do before diving into your job search.
NYC Data Science Bootcamp helps you avoid the risk of public job boards as well by offering access to corporate networking events, corporate partner projects and internships, a 2,000-strong alumni network, and more.
Does the Job Description Require Experience Unique to Data Science?
While tempting to give companies the benefit of the doubt, this can lead you down risky paths when applying for data science jobs. Data scientist roles pay well so it’s safe to assume any company hiring for one should know what skills the job needs.
Before applying, make sure the job description requires skills unique to data science like those mentioned in the chart below.
What to Look for in a Data Scientist Job Description
Programming languages like Python, R, and SQL
Business intelligence tools like Tableau and Power BI
Big Data technologies like Hadoop and Spark
Gathering, cleaning, managing, manipulating, and transforming data
Machine learning and natural language processing
Analytical and statistical modeling
Generating insights from data to drive decisions
Working with data across multiple departments and channels
Ability to explain data in a way anyone can understand
Visualizing data and insights for various departments
Presenting reports to department heads or shareholders
Ability to work independently
Although job postings will vary depending on the company and specific role requirements, the above are the general key functions of any data scientist. Even an entry-level data science job should require experience in Python, R, SQL, business intelligence tools, and statistical modeling with data.
How Long Has the Company Been in Business?
You’re investing your time and experience into a potential company, so make sure the commitment is worthwhile. You don’t want to sign onto a high-responsibility data science role at a mismanaged company.
Plus, fake job postings aren’t unusual. They are a common tool for collecting personal information for use in identity theft – especially highly paid roles like data scientists.
So it is best to research the company to make sure it is established and legitimate.
What’s the Market Outlook in the Company’s Industry?
A company’s future might seem optimistic and have excellent growth figures, but market forces tell another story.
Data science roles often pay over $100k per year. If the company goes out of business while the rest of the industry is struggling, other companies probably won’t have the budget to absorb a few more $100k salaries.
Healthcare, finance, technology, and catch-all roles like business operations are smart choices.
How Will the Data Science Job Advance Your Long-Term Career?
Even an entry-level job should offer some kind of leverage to build a long-term career. Some job descriptions will state the potential outright. If not, you can ask during interviews or glean information from the website.
Look for things like:
Whether or not you have a Ph.D., it’s important to assume that you’re competing with several other qualified data scientists for the same position.
Do everything you can to make the hiring manager’s job easier. This will ensure your application stands out while positioning your skills as the best fit for the role.
Students of NYC Data Science Bootcamp get resume advice and assistance from mentors and corporate partnerships.
Keep Your Data Science Resume Brief
PhDs or applicants with several years of experience often go overboard with their resumes or CVs. Update your resume for every new job application to ensure it only contains the necessary background information for the role.
Focus on outcomes you contributed to rather than just tasks and duties. Show the hiring manager you understand how you fit into the data science team and overall company.
Highlight Relevant Projects for the Job
Every data science resume needs a portfolio of projects to back up their skills and demonstrate their ability to complete the job’s duties.
This is one reason it’s so important to complete a wide variety of projects. Your portfolio should display your experience in various tasks and applications such as:
Highlight the most relevant projects in your portfolio for each job posting. Briefly explain how the projects apply to the job position and why they make you the best choice for the role.
List Skills Mentioned in the Job Description
Every smart data scientist should assume that a machine will analyze their resume before it ever reaches human eyes. Job applicant software uses algorithms to match keywords from the job posting with what’s inside your resume copy.
Make the AI’s job easier and avoid confusion. Look at the job posting and include your relevant skills in a bulleted list.
For this job description, your data scientist resume might include a list like this:
Don’t Forget Soft Skills
Many qualified data scientists get so caught up in technical skills that they forget to showcase their soft skills.
Although technical skills like programming and analysis are certainly required for data scientist roles, you won’t work in isolation. Data scientists need soft or transferable skills as well, so everyone across an organization can use their work.
Important Soft Skills for Data Scientists
Attention to detail
Making complex data concepts easy for anyone to understand
Patience while managing complex analyses
Working with teams to complete projects
Project presentations and reports
Comfortable with uncertainty
Written and verbal communication
Data scientist job interviews are daunting, to say the least. Whether you’re interviewing for your first entry-level data science job or have ten years under your belt in the industry, companies have high expectations for data scientists.
NYC Data Science Bootcamp students also get expert interview prep from mentors along with mock interviews. That’s why NYC Data Science was rated one of the top bootcamps on Switchup for five years running.
Stay Alert During Every Interaction
A company’s HR representative or recruiter might first call to set up the interview and ask a few basic questions.
Remember, teamwork and communication are critical skills for data science roles so stay friendly during the interaction.
Consider the Questions Each Role Might Ask
You won’t typically meet with each role during a single interview but you should plan to do so eventually. Data scientists work with each department across an organization so expect questions from roles such as:
With some digging, you might find common interview questions from the company on Glassdoor or Reddit. Depending on the role asking, questions might include:
Practice Your Skills
Companies will ask you to complete a coding challenge to ensure your skills match what the job requires. Most coding challenges are simple but may ask you to add your creativity so they can see how you operate without direction.
You can find coding challenges online for practice. Data science bootcamp, however, also provides projects and challenges to help you prepare for interviews.
Complete Mock Interviews
For such a high-pressure job, mock interviews are a smart way to get outside your head. Ideally, fellow data scientists and expert mentors should lead the mock interview and prepare the questions based on the company’s unique requirements.
NYC Data Science Bootcamp students and alumni get access to mentors and mock interview prep so they can enter the process confidently.
Most top data science openings — especially at leading companies — aren’t posted on public job boards. Companies prefer to hire candidates from trustworthy schools and other referral networks.
Use these tips for leverage to find the best jobs.
Membership in a relevant professional organization unlocks access to jobs. Also, stay consistent with attending data science events in your industry and add thoughtful connections to your LinkedIn network.
NYC Data Science Bootcamp boasts an alumni network of over 2,000 working data scientists along with comprehensive career assistance such as:
Bootcamps with corporate partnerships like NYC Data Science are often the first places companies turn to when they need data scientists for internships or live projects.
Companies know that students have over 400 hours of experience and the right skills training to meet the job’s demands.
If you have a degree in computer science or mathematics but lack the hands-on experience a data scientist job demands, a data science bootcamp can fill the gap.
NYC Data Science Bootcamp students learn how to master Python and R programming for data analysis, visualizations, machine learning, Big Data, and more. You’ll complete projects developed with help from leading companies that you can add to your portfolio.
Bootcamp students also get access to the alumni network of over 2,000, live project and internship opportunities, networking events with industry experts, and much more.
Learn how bootcamp can help you get a job in data science in as little as 12 weeks.
I founded and run NYC Data Science Academy and SupStat Analytics Inc. I was ranked as one of “9 Women Leading The Pack In Data Analytics" by Forbes in Aug 2016 and “top 50 data scientists in China” in Sept 2019. I enjoy meeting people and enjoy sharing experiences with young professionals and students. I am a data scientist who has many years of practical experience in data technologies and the analytics industry, after developing my expertise in statistical methodologies and software and in a variety of programming languages such as R, Python, Hadoop, Spark and etc.View all articles
NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.
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