Principal Data Scientist

Bayer•Chesterfield, MO

16h•$147,040 - $220,560

About The Position

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. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so 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 YOUR TASKS AND RESPONSIBILITIES 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.

Requirements

Strong academic background with coursework or research in machine learning, AI, statistical modeling, and data analysis
Proficiency in programming languages such as Python or R for data analysis and machine learning
Familiarity with libraries and frameworks like TensorFlow, PyTorch, scikit-learn, pandas, and NumPy Advanced knowledge of statistical methods and techniques
Experience in hypothesis testing, regression analysis, clustering, and classification
Expertise in machine learning algorithms and techniques Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark) Proficiency in SQL, relational database, and NoSQL databases
Excellent verbal and written communication skills
Experience working collaboratively in interdisciplinary teams
Demonstrated experience in leading and mentoring other data scientists
Ability to align data science initiatives with business objectives
Strong decision-making skills
Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related field or Bachelor’s with minimum 10 years experience.

Nice To Haves

Strong analytical and problem-solving skills.
Ability to evaluate assumptions and limitations of data

Responsibilities

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
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
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
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
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
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
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
Write and test scripts and create basic models in one or more languages
Collaborate on shared codebases, using a variety of methodologies
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