What Does a Data Scientist Do? Job Overview - HackerRank Blog

Data Science & Analytics What Does a Data Scientist Do? Job Overview & Skill Expectations Written By HackerRank | March 8, 2022 Every day, the world creates 2.5 quintillion bytes of data. That’s 912.5 quintillion bytes each year — a staggering rate that’s only accelerating. With this massive rate of data generation in mind, it should be no surprise that over 90 percent of the world’s data was generated in the last two years alone.  Data scientists are responsible for interpreting, modeling, and transforming this growing ocean of data into valuable and actionable information. In this post, we’ll break down the statistics, job requirements, and responsibilities of a career in data science. Overview of the Duties of a Data Scientist Companies of every size and industry need data to make business decisions. And businesses need people with knowledge of statistics and data modeling to unlock the value within this mountain of raw data. Data scientists use statistical analysis, data analysis, and computer science to transmute unprocessed data into actionable insights. On a more technical level, the core job responsibilities of data scientists include: Writing code to obtain, manipulate, and analyze data Building natural language processing applications Creating machine learning algorithms across traditional and deep learning Analyzing historical data to identify trends and support optimal decision-making Communicating with both technical and non-technical stakeholders  Keeping up-to-date with advancements in technology Working in an agile environment What Kinds of Companies Hire Data Scientists? Any company that’s looking to collect, manage, and interpret data to make business decisions will need to hire data scientists. With companies in every industry becoming increasingly data driven, the demand and opportunities for data scientists is endless. To name a few, these industries and applications include: Finance. Data science applications include risk management, fraud detection, algorithmic trading, and consumer analytics Healthcare. Data science applications include medical imaging, gene sequencing, predictive analytics, patient monitoring, and disease prevention  Insurance. Data science applications include risk pricing, customer profiling, call center optimization, and fraud detection Pharmaceuticals. Data science applications include drug development, patient selection, safety assurance, and targeted marketing and sales Retail. Data science applications include fraud detection, inventory management, product recommendations, price optimization, trend prediction, and sentiment analysis Supply chain. Data science applications include distribution, pricing, sourcing/procurement, and demand forecasting Telecommunications. Data science applications include network optimization, service personalization, sentiment analysis, and some customer experience Types of Data Science Positions The titles data scientists hold vary drastically, depending on their experience, education, and the company they work at. The title of a graduate from a coding bootcamp might look different than a candidate with a four-year degree. And the role of a data scientist in a five-person startup will be different than at a 5,000 person company. The role in which a data scientist starts their career will depend on their education level. Some data scientists will start out with an entry-level role like junior data scientist or data science analyst. A new data scientist usually works in one of these roles for one to three years. However, because many data scientists obtain master’s and doctorate degrees, a recent graduate might have enough equivalent experience to start their career in a senior-level role.  Data scientists that progress to or start in senior-level roles will hold job titles with hands-on analytical experience, such as senior data scientist, data scientist II, or senior data analyst. While they spend several years honing their skills, their responsibilities expand to include taking ownership of projects, working independently in a team environment, and mentoring project team members. Senior data scientists who have not completed a doctorate degree might pursue one to advance their career opportunities. Beyond experience and experience, data science titles also vary depending on area of specialization. Examples of specializations include: Data engineer Data architect Data storyteller Business intelligence developer Machine learning scientist Machine learning engineer With some experience under their belt, a data scientist often faces a crossroads in their career, having to choose between two paths.  The first path is to pivot into people and team management functions. Hiring, mentoring, resource planning and allocation, strategy, and operations become a larger component of the responsibilities of data scientists pursuing this career path. At the higher levels of an organization, these job functions might include: Director of Data Science Data Science Manager Data Operations Manager Information Systems Manager Chief Information Officer (CIO) Chief Technology Officer (CTO) The second possible career path is to continue as an individual contributor. Many data scientists opt to continue their careers as individual contributors, enjoying equally fulfilling careers and developing deeper technical expertise in various technology languages and frameworks. The motivation behind this decision is that experienced data scientists may not be interested in or qualified to be managing a team. And scientists in an individual contributor role have the opportunity to focus on growing their technical skills and learning the newest emerging technologies. However, the career path for senior data scientists working as individual contributors is still being defined, as data science is still a relatively new field. So, while data scientists are able to make a significant contribution early in their careers, the playbook for a career in data science is still very much being written.  Salary Comparisons & Job Outlook On average, data scientists tend to receive a salary significantly higher than the national average in their country. For example, in the U.S. the average salary in 2020 was $53,400. In contrast, the average base salary for data scientists in the U.S. is $110,000 to $129,000. That’s 105.9 to 141.6 percent more than the national average. Junior data scientists can expect to occupy a lower salary band at the beginning of their career. In contrast, senior positions provide a higher average compensation, though data for this specific salary band is hard to find. Industry and company size also affect the salary band dramatically. Historically, though, geography has had a significant impact on the compensation of technical talent — and that includes data scientists. The U.S. leads the world in data scientist salaries by a margin of 8.1 percent. The remaining members of the top five highest paying countries are Australia, Israel, Canada, and Germany. But compensation also varies within each country, not just internationally. For example, while data scientists in Seattle make an average of $147,900 a year, most data scientists in Chicago earn $120,448. That’s a 22.8 percent variation in compensation. The job outlook for data scientists is equally promising. As the quantity of data the world produces accelerates, so too will the demand for scientists to analyze that data. From 2020 to 2030, the U.S. Bureau of Labor Statistics projects the number of employed computer and information research scientists in the U.S. to grow by 22 percent — almost triple the 8 percent average growth rate for all occupations.  As data science is still a maturing field, the role of data scientists will continue to evolve. Data scientists will play a critical role in the development of the world’s most promising technologies, including, artificial intelligence, deep learning, natural language processing, robotics, and self-driving vehicles. Requirements to Become a Data Scientist Technical Skills Data scientists use a range of technologies to work with data. These include, to name a few: SQL MySQL NoSQL MATLAB Python Go Julia Ruby R Scala Many data science roles also require knowledge of at least one major programming language, such as C, C+, C#, or Java. And skills for cloud technologies such as AWS are in demand for certain roles, too. Data scientists also might use data visualization tools such as Tableau or front-end languages such as JavaScript to create visuals. Recruiters and hiring managers looking for data scientists should look for proficiency with in-demand competencies and frameworks. These include: Apache Spark (data processing) Hadoop (big data processing) Hive (data warehousing) Keras (neural networks) Pig (data analytics) PyTorch (natural language processing) TensorFlow (neural networks) It’s worth noting that there’s a degree of fluidity to the technologies that data scientists use. A framework that’s in demand today might be outdated a year from now. One skill emphasis that makes data scientists unique is mathematics. While a strong background in math is important to any programmer, it’s essential to data scientists. Data science is equal parts statistics and computer engineering, so while the job description might not mention it, competency in the following subjects is vital: Statistics Probability theory Classification  Regression Clustering Linear Algebra Calculus Technical recruiters and aspiring data scientists alike will notice that there’s no standard way to learn this skill set. There’s a huge variety in the technologies data scientists know and the order they learn them in. Soft Skills Technical competency alone isn’t enough to succeed in a data science role. Mathematical, analytical, and problem-solving skills are a must in any technical role. And soft skills are even more critical in a digital-first or digital-only environment. Employers may put even more stock into data scientists with strong soft skills, such as: Time management Communication Project management Problem solving Communication skills, in particular, are critical to data science. One of a data scientist’s main responsibilities is to communicate complex information to nontechnical stakeholders in other departments. The ability to translate technical subject matter into digestible, actionable information that anyone can understand is highly valuable to data scientists — and the teams that employ them. Experience & Education After competency, the most important qualification for data scientists is experience. On-the-job experience and training is a critical requirement for many employers. Then there’s education. While a university education is common in technical professions (about 75 percent of developers have a bachelor’s degree or higher), the field of data science tends to place a greater emphasis on postgraduate education. One study found that 88% of data scientists have a master’s degree or higher. Doctorate degrees are also common — and sometimes required. Recruiters interviewing for data science roles should anticipate that most candidates will have a postgraduate degree and that most employers will require a degree.  But competition for skilled data scientists is fierce, and it’s common for job openings requiring degrees to go unfilled. Companies looking to hire data scientists will have access to a much larger pool of talent if they recognize other forms of education and experience. Even in the field of data science, online training, bootcamps, and independent learning are popular ways to learn new technical skills.  Resources for Hiring Data Scientists Developer Hiring Solutions HackerRank Projects for Data Science Resources for Becoming a Data Scientist HackerRank for Developers Getting Started with Data Science