The hunger for data scientists won’t abate anytime soon. Even the most old-fashioned companies have awoken to the potential of granular data to transform how they do business. But the complexity of data analysis, combined with widespread demand, means those companies will need to pay top dollar for data scientists.
Dice’s Salary Calculator suggests a data scientist with five years’ experience can expect to earn between $103,500 and $134,571 in the Bay Area, and that’s before you throw in perks and benefits. Even away from the major tech hubs, these professionals can still expect to pull down quite a bit; in Kansas City, for example, a data scientist with the same level of experience could pull down between $87,000 and $114,000 in annual salary. And that’s before you factor in specialized skillsets such as predictive analytics.
According to this year’s Dice Salary Survey (PDF), specialization in certain data-centric platforms can translate into substantial pay. For example, those skilled in MapReduce (which allows data teams to process large datasets in parallel) can pull down an average annual salary of $125,009. Knowledge of HBase, Cassandra, Apache Kafka, and Pig could prove similarly lucrative.
And of course, there’s also Apache Hadoop, the “granddaddy” of Big Data. Although it’s become an overused buzzword among managers, Hadoop nonetheless remains a solid platform for running data applications on large hardware clusters. Hadoop’s usability factor has increased over the years thanks to a variety of customized distributions by top software vendors. Extensive knowledge of Hadoop is a key requirement of many higher-level data analytics jobs that pay six figures.
In addition to technical qualifications and certifications, data scientists need to communicate their findings in a clear, direct way to all sorts of stakeholders, from team members to senior managers. That’s one of the reasons why so many job postings for data scientists put “excellent soft skills” (or words to that effect) as a requirement.
Data scientists often boast a skillset that straddles applied mathematics, computer science, and statistics. On a more tactical level, knowing how to effectively “clean” a dataset to achieve accurate results is a key attribute; a good data scientist respects how messy a dataset can become, and has strategies to compensate for that.
“You are trying to leverage the data to answer a question. You are not trying to stretch it too far,” Crystal Valentine, vice president of technology strategy at Big Data firm MapR, told Dice earlier this year. “As a rule of thumb, gathering as much data as possible is a good strategy.”
If you’re set on pursuing data science as a career, consider a master’s program in data science, predictive analytics, or similar field; such programs often take one or two years. Some bootcamps also offer data-science programs, although not all employers give bootcamps the same “weight” when considering a candidate’s qualifications.
Last but certainly not least, don’t dismiss the value of networking. Look for meet-ups of data scientists in your area; they can not only provide some actionable tips about improving your skills, but give you some idea of which local employers are looking for data scientists at the moment.