Job Description Summary
The ITS Data Scientist is responsible for integrating business, information, and technology into analytical models that help drive business performance and competitive advantage and providing the business with answers to questions. The role collaborates with Business Analysts and Platform architects to create value from varied data sources. Creating value from data requires a range of talents: from data integration and preparation, to architecting specialized computing/database environments, to data mining and intelligent algorithm development.
This role is viewed as an expert in making sense of complex data environments, encompassing both business data and process understanding and technical expertise. Leads in developing innovative, technical solutions to important, highly complex strategic and operating problems. Has strong knowledge in business and technical functions that are touch points with their area of expertise. Provides technical consulting on complex projects. Acts as a source of direction, training and guidance for other team members. Is knowledgeable in industry best practices in their area of expertise and uses resources outside of KC to deliver solutions.
Development of advanced analytics to help drive competitive advantage from data with accountabilities across multiple functional and technical areas with wide range of complexity. The Data Scientist must understand complex data types (integrate, manipulate, prepare), know advanced analytics (appropriate techniques, interpret data and diagnose models, meet business requirements), and focus on the business outcomes (goals, constraints, decisions while communicating outcomes via presentations).Develop models and algorithms that drive innovation throughout the organization. This may include marketing, supply chain, inventory planning and deployment, network planning, order routing, and order fulfillment and delivery Conduct advanced statistical analysis to provide actionable insights, identify trends, and measure performance Build learning systems that monitor data flows and react to changes in customer preferences, network constraints, and business objectives Collaborate with engineers to implement and deploy scalable solutions Provide thought leadership by researching best practices, conducting experiments, and collaborating with industry leaders Partners as a bridge between the business and the information management teams to make sure that the solution fits within the data management principals Coordinates data science implementations while leading design variances based upon business needs while ensuring artifacts and repositories are documented Manages engagements with vendors as they relate to evaluation, design and delivery of business capabilities Contributes to the evaluation and selection of software product standards Leader in industry representation, policy formation, User Groups, and strategic direction
Mentors others to complete Continuous Improvement (CI) initiatives; consults and shares knowledge across org; awareness of industry trends.
Education required/ preferred:
Communication: Data scientists must communicate effectively up and down the data supply chain: first, to obtain access to the data they require; second, to work with those who understand the business meaning behind the data; and third, to articulate findings and implications to business leaders in language they understand. A data scientist must be able to use data to tell stories. Key components of these communication skills are those of persuasion and expectation management. The ability to insert themselves into core business functions and assert their ideas is therefore critical.
Collaboration: Working directly for business leaders and side-by-side with business unit personnel, they need to shed the introverted statistician stereotype. Increasingly, business professionals require access to analytic techniques beyond basic math and must be able to rely on the data scientist to work closely with them. The data scientist enables the broad consumerization of derivative result sets and analytics (if not the raw data). The data scientist must have the ability to juggle competing priorities and pressures.
Leadership. The role of the data scientist can incorporate data oversight responsibilities including directing the efforts of teams of consultant statisticians, data administration and integration professionals, and data visualization, reporting, and application integration developers.
Creativity. The work of the data scientist is very much an innovation-oriented exercise in solving open-ended conundrums. Data scientists are tasked with finding opportunities to optimize, expand or transform the business through the lens of information. Moreover, data scientists must be creative in sourcing data, modeling problems and employing a range of analytic techniques.
Discipline. Although creativity is critical, data scientists must remember that "science" is
part of their directive. This means following established scientific methods, employing legitimate techniques, using valid data and embracing causality. Scientific methods demand that questions are well-defined, true data (observations) is collected, and hypotheses are formed, investigative methods are selected, data is analyzed and interpreted with yielding conclusions, and results are formally communicated and tested. Although rigid methodology is recommended, results perfection is not. Business opportunity costs in a fast-paced marketplace are too high to spend excessive time achieving incrementally better analyses. However, a data scientist — just as any good statistician or other analytics professional — must understand the differences between correlation and causality and between incidental and insightful patterns.
Passion: An obsession for information, solving insurmountable problems and finding unique ways to accelerate the business.
Consultancy: Manages provision of specialist knowledge over a range of topics in data science including the role of IT in the business; in own areas of expertise provides advice and guidance influencing the effectiveness of the organization’s business processes.
Data Design: Controls analytics data design practice within the enterprise. Influences industry-based models for the development of new technology applications. Develops effective implementation and procurement strategies, consistent with business needs.
Data Analysis: Sets standards for advanced analytics tool usage and techniques, advises on their application, and ensures compliance. Manages the investigation of corporate data requirements, and co-ordinates the application of data analysis and analytics techniques, based upon a detailed understanding of the corporate information requirements, in order to establish, modify or maintain analytical models and their associated components.
Autonomy: Has authority and responsibility for all aspects of data science, including policy formation and application. Is fully accountable for actions taken and decisions made, both by self and subordinates.
Influence: Makes decisions critical to organizational success. Influences developments within the IT industry at the highest levels. Advances the knowledge and/or exploitation of IT within one or more organizations. Develops long-term strategic relationships with customers, partners, industry leaders and government.
Complexity: Performs highly complex work activities covering technical, financial and quality aspects. Contributes to the formulation and implementation of IT strategy. Creatively applies a wide range of technical and/or management principles.
Business Skills Absorbs complex technical information and communicates effectively at all levels to both technical and non-technical audiences. Assesses and evaluates risk. Understands the implications of new technologies. Demonstrates clear leadership and the ability to influence and persuade. Has a broad understanding of all aspects of IT and deep understanding of own specialism(s). Understands and communicates the role and impact of IT in the employing organization and promotes compliance with relevant legislation. Takes the initiative to keep both own and subordinates' skills up to date and to maintain an awareness of developments in the IT industry.