Statistical/ IT Skills A Data Scientist must have or be able to quickly acquire a detailed knowledge and understanding of Big Data statistical methodology, concepts and research as they apply to the production of official statistics. S/he must be comfortable with numbers and have an appreciation and understanding of information presented in mathematical terms; have the ability to extract the key messages or underlying trends present within data; and be able to present statistical results and concepts, both orally and in writing, in a confident and professional manner. Has knowledge of recent big data technologies and techniques like Apache Hadoop, Spark, Hive, Pig and others. Is willing to work with various data processing techniques using statistical packages and if required developing software for data processing tasks (in programming language of choice i.e. Java, Python). Experience and knowledge in the use of Machine learning, Map reduce and R would also be advantageous. Can join various data processing techniques to achieve given analytic task; Is ready to operate on unstructured data with wide range of tools (from software development to computational packages) Has willingness to be up to date with newest trends in big data techniques and technologies Has knowledge of data visualization techniques Has a detailed knowledge of statistics and statistical theory and is able to apply it; Has a keen interest in data analytics and is inquisitive. Is comfortable working with numbers, tables and algorithms; Ability to code algorithms for data processing in suitable technologies/environments. Can quickly analyse complex data, identifying what is relevant; Assimilates information from a range of sources; Strives to understand what the results mean; Knows what s/he wants to achieve from his/her data analysis rather than just the procedures to be followed;
Delivering Quality and Ethical Analysis A Data Scientist must maintain the highest levels of integrity when carrying out his/her work. S/he will continuously check, cross-check and quality assure work to ensure accuracy and to produce technically accurate figures and work of a high standard. An effective Data Scientist will anticipate problems and test data to ensure it makes sense. Understands the methodology for processing Big Data Has a knowledge of standards for processing Big Data Has the ability to work with text analytics Understands Data Governance in the context of Big Data Understands the importance of documentation in relation to Big Data processes Maintains the highest levels of professional integrity; Maintains the privacy of data at all times Can quickly identify any anomalies or errors in the data; Continuously checks work to ensure accuracy; Cross checks data against other sources; Is comfortable dealing with some ambiguity in data; Has an appreciation of the nuances in statistical data; appreciating the importance of the intricacies and interaction between data; Produces technically accurate figures, quality assuring all elements of his/her work; Is proactive, anticipating potential problems within the statistical analysis and the data and addressing these; Tests the data and outputs to ensure it makes sense, making estimations if required; Pays close attention to detail and is aware of the importance of producing high quality data; Produces work of a high standard, even when tasks are routine;
Process Management Skills & Creative problem solving An effective Data Scientist will be structured and methodical in his/her approach to work. S/he will have strong project management skills, with the ability to prioritise and make decisions in a timely manner. S/he takes responsibility for his/her own work and can come up with solutions to the problems encountered. Is structured and methodical in his/her work, monitoring work to ensure completion; Has strong project management skills with a focus on meeting deadlines and getting the best use out of the available resources; Adopts a logical approach to his/her work, breaking tasks down and delivering results; Continually reviews processes to make improvements; Can manage and prioritise multiple tasks; Is prepared to make decisions in a timely manner; Comes up with solutions to the problems that they encounter; Is innovative and creative Takes responsibility and ownership for his/her own work, even when something goes wrong; Works effectively under the pressure of very clear deadlines; Consistently delivers projects successfully and in a timely manner;
Teamwork A Data Scientist must work collaboratively with others, building networks and communicating clearly with them. When required, s/he can convince others to supply information and will provide advice and assistance when the need arises. An effective Data Scientist will support his/her colleagues and will take the views of others on board. Works collaboratively with others internal and external to the organisation; Builds up networks with relevant people in the field; Convinces others of the importance of supplying information to the organisation; Is prepared to speak out in an assertive but non-confrontational manner; Provides advice and assistance to others; Can clearly communicate his/her views and requirements to others; Is open to others reviewing his/her work; Can listen carefully and take on board other people s views; Supports his/her colleagues, even if they are working in a different area/project; Is willing to adapt his/her own approach depending on the requirements of the team; Is comfortable interacting and talking to people and working as part of a team;
Communication of Information A Data Scientist must produce objective information in a fluent manner and following a logical structure. S/he will provide data in a manner that is appropriate for the target audience and can project a credible and confident image to internal and external parties. S/he takes steps to ensure that people do not misinterpret data and outputs, anticipating where this may occur. Produces objective information, free from personal opinion; Makes an informed decision on whether information can be released to external parties, consulting when appropriate; Communicates fluently in writing, producing reports that follow a logical structure; Is confident defending his/her work, articulating arguments coherently; Is able to summarise data in a manner appropriate for the audience, translating complex information into simple terms; Explains figures and analysis in simple terms; Projects a credible and confident image to internal and external parties; Can communicate and represent the organisation in formal settings, e.g. dealing with the media; Ability to design data outputs to meet the requirements of stakeholders; Anticipates likely questions and queries falling out of the data; Takes steps to ensure that people do not misinterpret data and outputs, anticipating where this may occur;
Developing Statistical Expertise A Data Scientist must develop expertise in his/her area of specialism but also be open to learning new skills to perform in a new environment. S/he will be flexible in his/her work and willing to work outside of his/her comfort zone. An effective Data Scientist will take on board feedback from managers and colleagues. Develops expertise in his/her area of specialism; Open to change and taking on new tasks in his/her work; Can quickly learn new skills or understand new information in order to perform effectively in a new environment; Takes on board feedback or advice from his/her manager and colleagues; Considers learning new skills to be a key part of his/her job; Is willing to work outside of his/her comfort zone; Is adaptable and flexible in his/ her work; Keeps up to date with developments in his/her field of expertise;