Principal Data Scientist

Overview

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where Health for all Hunger for none is no longer a dream, but a real possibility. There are many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Principal Data Scientist

The primary responsibilities of this role, Principal Data Scientist are to:

A principal data scientist has complete oversight of data science within a platform. Individuals in this role will:

• Set direction; build capability; oversee resourcing, budgeting, professionalism and outputs and products; support and enable future IT developments; understand and use a wide range of data science techniques, tools and technologies; lead on ethics; communicate and present data science and data ethics effectively to senior leaders; champion the role of data science in supporting organizational priorities and in collaborative working across platforms and functions;
  
  represent the platform on data science matters.
• Applied Math, Statistics, and Scientific Practices:
  Identify opportunities to develop statistical insight, reports and models to support organizational objectives, while collaborating across the organization effectively.
• Critique statistical analyses.
• Use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle.
• Use model outputs to produce evidence and help design services and policies.
• Understand a broad range of statistical tools, particularly those deployed within the organization, and can use these appropriately and help others to use them.
• Data Engineering and Manipulation:
  Work with data engineers and data scientists to design and deliver products into the organization effectively.
• Understand the reasons for cleansing and preparing data before including it in data science products and can put reusable processes and checks in place.
• Access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organization.
• Data Science Innovation:
  Demonstrate practical knowledge of data science tools and techniques.
• Develop data science solutions that maximize insight; identify opportunities for how data science can improve data practices.
• Delivering Business Impact:
  Champion the role of data science within the organization; understand and champion user research, and can design and manage processes to gather and establish user needs; identify and create opportunities to develop and deliver data science products to support organizational objectives, while collaborating across the organization to fulfil meaningful goals.
• Take responsibility for delivering scalable data science products into the organization, and establishing maintenance support.
• Developing Data Science Capability:
  Act as a leader or technical specialist, providing detailed support and guidance within the organization and helping colleagues to develop skills; set the direction of continuous development plans within the team; keep up to date with new developments in data science and can match those to opportunities in your organization; talk confidently about the benefits of data science approaches to existing and potential customers.
• Demonstrate an in-depth understanding of a wide range of data science techniques, such as machine learning and natural language processing, and detailed knowledge of at least one specialty; use these techniques to build data science solutions, including reports, models and dashboards.
• Ethics and Privacy:
  Oversee compliance with data ethics standards and legislation; develop and manage the ethical framework for how data, machine learning and artificial intelligence techniques are used across the organization, ensuring data governance complies with relevant legislation and standards; embed a culture of data ethics and explain why this is so important; ensure ethics guidance is appropriately applied to the formulation, implementation and evaluation of policies and programs;
  
  assess and constructively challenge proposed policies and programs.
• Programming and Build:
  Write and test scripts and create basic models in one or more languages; collaborate on shared codebases, using a variety of methodologies.
• Understanding Product Delivery:
  Understand the differences between delivery methods, such as Agile and waterfall, and can choose the most appropriate method to deliver each product; define the minimum viable product (MVP) and support decisions about priorities; work with specialists in multidisciplinary teams to smoothly deliver data science products into the organization.

Bayer seeks an incumbent who possesses the following:

• Strong academic background with coursework or research in machine learning, AI,…