Data scientists are experts in business, math, technology, and social science. Working across industries, they identify questions that need asking and extrapolate the most relevant data from vast seas of information. Data scientists challenge existing assumptions and overcome skepticism using statistics, trend reports, and visual presentations. Data science guides informed decision-making, helping executives develop corporate action plans to solve core business challenges.
Data science guides informed decision-making, helping executives develop corporate action plans to solve core business challenges.
Data Scientist Job Description
The most generalized job description, “data scientist,” calls for a candidate with business acumen, specialized skills, and leadership abilities that can guide a project from start to finish. Types of data scientists include data analysts, data engineers, data architects, database administrators, machine learning specialists, business intelligence developers, and many more.
Data analyst: A staple in many modern organizations, these professionals visualize and transform data into digestible communications the business can use to convey trends and ideas.
Data engineer: A data engineer creates, manages, and stores the data collection pipelines that fuel analysis.
Data architect: Data architects work with engineers to ensure all information is well-formatted and accessible.
Database administrator: Database administrators create and maintain business systems, databases, and recoveries.
Machine learning specialist: A machine learning scientist or engineer studies predictive modeling and machine learning algorithms to build neural networks that adapt to change as a human might.
Business intelligence developer: Business intelligence developers build systems to aggregate consumer and revenue data in formats that are quick to retrieve and ready to use.
Due to the dynamic and evolving nature of this field, new and highly specialized data science job descriptions are created often.
Data Scientist Skills
Data science professionals have a robust skill set of technical competencies and specialized proficiencies. Technical data scientist skills vary by role and employer, but may include proficiency in:
Big data platforms (Cloudera, Microsoft Azure, MongoDB, Oracle)
Data mining and visualization (D3, GLM/Regression, Java, Periscope, Python, R libraries, Tableau)
Math and statistics
Machine learning (decision trees, classification, clustering, natural language processing)
Third-party analytics (AdWords, Coremetrics, Facebook Insights, Google Analytics, Site Catalyst)
The hard skills you’ll need in this field can be taught and are built into most college curriculums. However, you’ll likely need soft skills like critical thinking, problem-solving, and communication as well. Data scientists often work as part of a team, giving presentations and leading projects. In an advanced data science degree program, candidates develop business management skills, viewing data as a means to business efficiency and innovation. A combination of core and elective courses will also allow them to hone their storytelling abilities, using data to highlight a compelling narrative. Data scientists are students for life, always learning and adding to their skill sets as technology advances.
Data Scientist Responsibilities
So, what can you expect from your day-to-day as a data scientist? As in most fields, data scientist responsibilities vary across roles and industries, among other factors. Below is a high-level overview of the potential responsibilities data scientists may be given by level of experience.
Entry-level data scientist: Starting out as an associate or junior data scientist, you’ll learn all you can about a business while working alongside more experienced colleagues, and work on improving core technical skills like SQL and Python. Junior data scientists may brainstorm on architecture and strategy, but they are primarily responsible for completing specific tasks as directed by managers. You might start out building scripts or prototypes for data visualization projects. Your tasks will often have a defined scope and achievable objectives.
Mid-level data scientist: After graduate school or following a few years of workplace experience, mid-level data scientists may lead projects or teams. Generally, they tackle more ambiguous tasks, applying their advanced level of data science knowledge to complex business challenges. While a junior data scientist may create the SQL queries for an ETL pipeline, a mid-level data scientist can architect the entire ETL pipeline from start to finish and apply it to a machine learning model.
Senior-level data scientist: These data scientists can architect solutions to business challenges from beginning to end, delegating tasks to others, supervising a project in its entirety, and reporting to a VP or C-suite executive. Senior data scientist responsibilities may include onboarding people to new projects, communicating technical concepts, mentoring junior employees, ensuring top-quality code, and determining the prioritization of data science applications.