Senior Manager Analytics and Data Science Insights - Global CS Central Operations
The Senior Manager of Data Science & Analytics assembles and organizes people, technology, and processes necessary to address the current and future Data Science & Analytics needs of the business area they support. The successful candidate understands that information and data are key business assets and operationalizes the application of data and information in order to deliver optimal performance. They are a member of the business leadership of their area and actively influences data capabilities and competencies within the business.
The successful candidate should have a working knowledge and prior experience with the technical aspects of Data Science and related Technology. They are able to work with a diverse set of stakeholders to identify opportunities for a Data Science driven approach to business problem solving. Furthermore, they should be able to coordinate across multiple business units to identify the right team to execute projects or build products.
Plays an active supporting role in the definition and execution of all processes regarding Data Science & Analytics roles across the company (hiring, performance management, role definition and calibration, career path definition, help drive engagement with the Data Science & Analytics community)
Thinks strategically and identifies areas where Data Science & Analytics can make a significant impact on the business
Builds a strong team within their area, by coaching and developing managers as well as top technical talents.
The successful candidate will manage the Data Analytics and BI teams
Plays an instrumental role in evolving and designing new applications of Data Science & Analytics to business problems
Takes ownership and understands goals and objectives of supporting business partners
Brings vision and strategy to improve efficiency in the way Data Science & Analytics teams work
Drives implementation and deployment of complex projects within their scope
Is capable of articulating complex concepts in simple terms to senior stakeholders
Is the advocate of Data Science & Analytics throughout their scope/function
Works closely with other internal entities to ensure their teams are always working towards what’s most important for customers and to achieve high business impact
Ensures clear and effective communication to their stakeholders: proactively keeps all stakeholders informed of what’s happening, upcoming changes, projects, issues, etc.
Prior experience and a working knowledge of handling large data sets in multiple Data Science areas - Statistical Methods (Testing, GLM, Inference), Statistical/Machine Learning (Dimension Reduction, Clustering, Neural networks, Regularisation, Bagged/Boosted methods), Time Series, Natural Language Processing etc
Working knowledge of Python/R, Hadoop, SQL, and/or Spark (or similar big data technologies).
Working knowledge in Cloud based environments, specially AWS
Masters or PhD in Statistics, Mathematics, Econometrics, Physics, Computer Science
5 - 8 years of related experience in Data Science, analytics, analysis, performance reporting and data management
2 - 3 years general management experience in complex large multinational operations
Strategy & Communication:
Ability to design and deliver formal presentations that inform and influence people to respond in the desired way, by applying story-telling techniques and tailoring language and approach to various audiences.
Ability to identify key stakeholders of a project, plan or decision, by applying an understanding of how the parts of the organization and people are impacted.
Ability to create buy-in with stakeholders by effective communication, providing information and addressing their needs and concerns.
Ability to analyse the impact of the change brought by the project on stakeholders and design a change management approach by applying change management principles and communication strategies.
Prioritisation & Execution:
Ability to create a Data Science approach that addresses relevant business or technical problems, by applying understanding of the existing business and technical context and translating it into a data problem.
Ability to prioritise the most impactful business problem to address business needs, by assessing feasibility, scalability, MVP and complexity of a data analytics solution.
Ability to identify the most feasible approach, by applying knowledge of the possibilities and limitations that the different types of data analysis and/or data science modelling bring.
Ability to identify and source data required for the solution, by understanding which data type is needed (e.g. structured or unstructured), how to collect and organise that data and how to prepare it in a way that makes it fit for use.
Ability to identify the most effective data collection method for the goal you are trying to reach, by applying knowledge of the different types of data collection and their implications and limitations.
Ability to structure data taxonomy, by using understanding of the data and its hierarchical relationships.
Ability to ensure data quality and integrity, by managing data from distinct sources in order to maintain accuracy.
Ability to ensure quality of own and others’ data models, by making them reproducible and replicable and by validating through peer review.
Ability to create readable, discoverable and reproducible datasets and statistical models, by applying varied techniques and tooling (like Tableau, Python and R).
Ability to draw meaningful insights from complex data sets, by analysing patterns and synthesising data for business purposes using analytics techniques and tools.
Ability to derive and convey insights from data in an effective and visually compelling way, by making use of data visualisation tools (such as Tableau) and techniques, and by choosing the most appropriate visual representation that best shows the conclusions to be derived from the data.
Ability to objectively evaluate the impact of any given development, by designing and interpreting quantitative experiments.
Experience with data-driven product development: analytics, A/B testing, etc.
Experience with a range of Data Science tools and technologies, including BigData technology and Cloud native tools (esp AWS)