Data Scientist

Working Schedule
 

• Data Scientists can expect full-time work, with routine schedules. As a demanding field, it is imperative to keep skills sharp, which may require “off-duty” refresher training, additional certification, and other non-paid learning experiences. 

일반적인 업무

• Apply knowledge and skills to design new technologies or to maximize the use of existing ones
• Work with leadership, engineers, and external clients to identify problems 
• Assist organizations with policy development and goal attainment strategies
• Collaborate with teams and manage tasks and suspenses
• Depending on specific roles, Data Scientists might work with Python, R, SQL, databases, analytic tools, machine learning, and Big Data
• Develop feasible solutions to complex software or business issues
• Develop new theories and models 
• Identify hardware and software needs by talking with end-users, customers, staff, and stakeholders 
• Review project concepts and offer suggestions for improvements

추가 책임

• Engage in multidisciplinary projects
• Run tests of new systems
• Participate in professional organizations to share research and findings

소프트 스킬

• Able to predict problems 
• Active learning 
• Analytical
• Attention-to-detail
• Ability to contextualize ideas for others
• Creative and curious
• Critical (but flexible) thinking
• Decisive
• Focus
• Orderly
• Persistence and resilience
• Problem Solving 
• 올바른 판단
• 강력한 커뮤니케이션 기술

기술 능력

• Web platform development software: SQL (Structured Query Language), Python, Tableau, R
• Communication skills: need to be able to take the statistics derived from data gleaning and explain it in layman's terms to other branches of the corporation, such as IT and finance
• Teamwork: you will be a go between for various departments who are in need of the information gleaned from the data in order to make decisions
• Data/statistic analysis: Need to analyze and apply user feedback and research
• Good vision 
• Able to analyze and evaluate systems 
• Basic understanding of sales and marketing, customer service, telecommunications, design, management principles, and engineering science
• Business intel/data analysis programs, such as IBM Cognos Impromptu, Oracle Business Intelligence Enterprise Edition, QlikView, and Tableau
• Compiler software, such as Polaris parallelizing compilers, Greenhills Ada compilers, and LLVM compilers
• Database management and user interface/query programs
• Development environment software, such as LabVIEW, Apache Kafka, or Eclipse IDE
• Graphics imaging
• Knowledge of analytical and scientific software, such as Minitab, StataCorp Stata, and MATLAB
• Object or component oriented development software (C++, ActiveX, Java, Python)
• Strong math skills, including algebra, geometry, calculus, and stats

• Academic institutions
• Banking and financial sectors
• Cloud services and hosting
• Consulting
• Software as a Service (SAAS), web, mobile
• Computer software publishers
• Delivery companies
• Federal government/military agencies
• Large corporations
• Retail companies
• Insurance agencies
• Telecommunications
• Other research and development organizations
• Private specialized companies
• Search/social networking

• An understanding of machine learning: a method of data analysis that automates analytical model building. Machine learning allows computers to find hidden insights using algorithms that learn from data without explicit programming
• The merging or data science and IoT: the growing network of physical objects that feature an IP address for internet connectivity, and the communication that occurs between these objects and other Internet-enabled devices and systems.
• Ability to work with RIL (radio interface layer), which goes hand-in-hand with IoT
• Use of data science in healthcare: data gleaned by these scientists can be used to predict disease trends i.e. the resurgence of Ebola

Most organizations employing Data Scientists do so because they rely heavily on the skills and information those workers bring to the table. In some cases, Data Scientists are literally developing the products or services being sold, while in other situations they’re helping companies determine what to sell, who to sell it to, and how to sell more. As a result, expectations run high, but financial compensation is also high! 
 
According to the University of Wisconsin, entry-level workers earn up to $95,000, mid-level workers earn a median of $128,750, mid-level managers can make up to $185,000, and experienced manager median salaries can reach as high as $250,000. Clearly organizations paying these amounts will demand top-shelf expertise and value-added work. Therefore a major sacrifice, if it can be called that, is for Data Scientists to reach and maintain proficiency in all areas, and to keep up with (or ahead of) new and developing technologies. This can take a massive, long-term commitment, with much work done “after hours” on one’s own time. 

Data Scientists were almost certainly interested in computers at an early age, as well as puzzles and problems they had to solve or otherwise work out. They love intellectual challenges where discovering information and how to use it are the keys to winning. Some might have enjoyed reading or watching mysteries and detective stories. They’re interested in psychology, how people think, what motivates them, and how computers can find ways to understand people better on different levels, from individuals to groups and even whole societies. 
 
Most likely, a good Data Scientist was someone who, growing up, could see the “big picture” and how small elements, factors, or decisions could have large consequences. They may appear to be highly organized, but in their heads information is not static, it’s malleable and flexible. In other words, they can generally “think outside of the box” in order to creatively use data in ways others might not have thought of. They were also probably good at explaining technical things in layman's terms.