Special Feature

Everything you need to know to land a job in data science

Data engineers, predictive modelers and machine learning professionals need good storytelling skills in addition to technical expertise.

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TikTok, the CIA, Yeti Coolers and AstraZeneca Pharmaceuticals are all looking for data scientists. Salaries for data scientists range from $50,000 to more than $150,00, and companies want full-time data wizards as well as data engineers who will work on contract. 

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What does it take to get hired? Organizations are looking for job candidates with a bachelor's or master's degree in computer science, as well as experience with data modeling tools, XML, Python, Java, SQL, AWS and Hadoop.

SEE: Research: BI and data analytics usage up; but companies lack skills needed to take full advantage of tools (TechRepublic Premium)

Many data scientist job descriptions also mention the ability to work with a distributed and fast-moving team. Interpreting data for colleagues in business units is increasingly important as well. 

Ryan Boyd, head of developer relations at Databricks, said that data science will soon be a commonplace skill outside engineering and IT departments as data becomes increasingly fundamental to businesses.

"To stay competitive, data scientists need to be equally as obsessed with data storytelling as they are with the minutiae of data software and programs," said Boyd. "Tomorrow's best data scientists will be expected to translate their know-how into actionable insights and compelling stories for different stakeholders across the business, from C-suite executives to product managers."

Whether you are looking for your first data science job or figuring out your next career move in the field, the following advice from hiring managers and data science professionals will help you plot a smart and successful course.

SEE: How to become a data scientist: A cheat sheet (TechRepublic) 

Find the right data science role

Anand Karasi, founder of Budgets.ai, said that behind every good data scientist there is often a team of very good data engineers.  

"In most projects, more than 80% of the work involved is data engineering," Karasi said. "It's critical that the data is very accurate and clean, otherwise the models will be inaccurate in their predictions."

Karasi said that data scientists at Budgets.ai scan the web and use AI to compute corporate budget spend on AI/ML projects. In the last year, the company increased its spend on speech recognition (68%), video surveillance (69%), transfer learning (99%) and recommenders (122%). 

On Dice, there are many more postings for data engineer jobs compared to data scientists -- 1,997 versus 346. Companies are also looking for data architects, predictive modelers, data storytellers, business intelligence developers, and machine learning experts. Managing and analyzing data is a team effort, as these varied roles show.

Jen Hsin, head of data science at SetSail, an AI-powered sales platform, said that data science teams can have diverse areas of responsibility.  She describes these roles:

  • Analyst: Extracts insights and trends from business intelligence, revenue reports, or product use
  • Statistician or researcher: Uses hypothesis testing, confidence interval calculation, sample size estimate, evaluation of measurement errors, causal analysis and other tools to develop forecasts and other insights
  • Data engineer: Gathers, combines and organizes data sources to construct new data sources
  • Machine learning scientist: Builds machine learning models to solve business problems or enhance product
  • Natural language processing or computer vision specialist: Uses deep learning methods to build algorithms for text or image processing

Don't be intimidated

Hsin said it's easy to see why preparing for a data science career can be intimidating.

"Being a generalist who can cover several specialties can make one a more efficient data scientist," she said. 

She suggested identifying one or two areas that have the biggest overlap with work experience and personal interests to create 'starter' profiles.

"When job searching, read through the job description carefully to identify if the roles and responsibilities are a good match," Hsin said. "Meanwhile, continuously grow your skillset, before landing a job as well as on the job." 

SEE: 7 data science certifications to boost your resume and salary (free PDF) (TechRepublic) 

Build a resume

Alicia Frame, director of Graph Data Science at Neo4j, said that the best resumes don't necessarily come from people with doctorate degrees in computer science from Stanford.

"They come from people who can show that they've identified and solved problems and can clearly communicate their impact at a high level," she said. 

Frame also looks for experience in mentoring, teaching, and supervising and recommended that job candidates add a publications section on their resumes. This should highlight peer-reviewed journal articles, non-peer-reviewed articles, blog posts, presentations and media mentions.

Boyd recommended that people looking for data science jobs should highlight the ability to make data understandable and actionable, and focus on telling a story about a data set and what it means in the bigger picture.

"Explore different mediums like Powerpoint or interactive reports to do so, hitting key points that will move your audience to action," he said. "Data storytelling makes new information easily accessible and actionable, and your organization will benefit from it."

Experience with data visualization tools like Redash makes for a great addition to a resume, Boyd said. 

SEE: Top 5 programming languages for data scientist to learn (free PDF) (TechRepublic)
 

Landing a job in data science

Frame said she receives hundreds of resumes featuring the same five class projects that everyone seems to work on for a master's degree in data science. 

"I don't interview those candidates -- I look for people who've taken the initiative in whatever role they have," she said. "People who know how to shape and execute projects, communicate to stakeholders, and explain why the work they've done matters."

She suggested that job seekers ignore online credentials such as MOOCs and Kaggle competitions.

"Instead, I recommend you look for opportunities to take on data science projects and tasks in the role you currently hold," she said. "Data science is all around us, so finding those interesting projects within your current work can go a long way."

Karasi said that tools available for data scientists are exploding and that fairly good models can be developed by software engineers by using out-of-the-box tools like AutoML from Google. 

"Microsoft, Google and Amazon offer great tools that leverage the cloud infrastructure, and getting trained on these offerings and getting a certification is recommended,"  he said.

Frame also noted that networking and building relationships with colleagues is a key element of building a successful career path. 

"I know I can call on a colleague from 10 years ago, and they'll help me answer a question, or put me in touch with someone who can, and I think everyone who works with me knows that I would do the same for them," she said. "Caring about relationships creates the trust and respect that is often missing in the workplace and beyond."

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