Principal Data Scientist

Bayer•Whippany, NJ

2d•$151,120 - $226,680•Onsite

About The Position

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, and represent the platform on data science matters. This role will be located in Whippany, NJ. Candidates must live within reasonable commute to the Whippany, NJ site as remote working arrangements will not be accommodated.

Requirements

Minimum of a Master’s Degree in Computer Science, Statistics, Mathematics, Data Science, or a related field, or a Bachelor’s Degree with minimum 10 years of related experience.
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.
Hands-on experience designing or implementing knowledge graphs and ontologies using RDF, OWL, and SHACL, including experience applying Generative AI techniques to support their creation, enrichment, mapping, or validation.

Nice To Haves

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

Responsibilities

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.
Demonstrate strong knowledge of knowledge graph and ontology concepts, including semantic modeling, graph-based data integration, reasoning, and metadata management.
Apply semantic web standards and technologies, including RDF, OWL, and SHACL, to design, implement, validate, and govern enterprise ontologies and knowledge graphs.
Use Generative AI and machine learning algorithms to accelerate ontology and knowledge graph development, including concept and relationship extraction, entity linking, taxonomy and schema generation, mapping, enrichment, and quality validation.
Design human-in-the-loop processes to review, evaluate, and improve GenAI-generated semantic assets for accuracy, traceability, consistency, and business relevance.
Collaborate with domain experts, data architects, data engineers, and AI engineers to integrate knowledge graphs and ontologies into scalable data and AI products.
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.