Principal Data Scientist

4 weeks ago

Philadelphia, United States Elsevier Full time

Elsevier employs 9,200 people worldwide, including over 2,500 technologists. We have supported the work of our research and health partners for more than 140 years. Growing from our roots in publishing, we offer knowledge and valuable analytics that help our users make breakthroughs and drive societal progress. Digital solutions such as ScienceDirect, Scopus, SciVal, ClinicalKey and Sherpath support strategic research management, R&D performance, clinical decision support, medical education, and nursing education. Researchers and healthcare professionals rely on over 2,800 journals, including The Lancet and Cell; 46,000+ eBook titles; and iconic reference works, such as Gray's Anatomy. With the Elsevier Foundation and our external Inclusion & Diversity Advisory Board, we work in partnership with diverse stakeholders to advance inclusion and diversity in science, research and healthcare in developing countries and around the world.


The Principal Data Scientist will perform complex research, design, and development of algorithms and models for production. You should have deep expertise in a relevant field such as natural language processing, information retrieval, or generative AI. As an experienced Data Scientist you will work closely with stakeholders to understand the user problem that needs to be solved and with software engineers to put those solutions into production for our users. They will be passionate about using data science to solve real problems that impact patients’ care and outcomes.


Our Data Science group is a diverse group that uses a wide range of AI, machine learning, and statistical methods, with an emphasis on applied data science driven by user problems and product & commercial priorities. We support innovation projects early on in ideation and discovery as well as operationalizing data science, working in an embedded model with engineering squads to develop and support data science services in production. Our problem space is largely about how to process and represent the knowledge in our documents and content to support our clinician users.


The Principal Data Scientist will perform complex research, design, and development of algorithms and models for production. You should have a deep expertise in a relevant field such as natural language processing, information retrieval, or generative AI. You will work closely with stakeholders to understand the user problem that needs to be solved and with software engineers to put those solutions into production for our users. You will be passionate about using data science to solve real problems that impact patients’ care and outcomes.


Responsibilities

  • Understanding doctor, nurse, and patient (user) information needs to design data science, data analytics and machine learning solutions.
  • Serving as domain expert in relevant analytical frameworks. Designing approaches and writing code and services for data science and data analysis projects in a data science development life cycle
  • Collaborating with cross-functional teams in a diverse and multicultural environment to identify approaches for data science to improve our products or inform our customers.
  • Contributing to a collaborative, inclusive working environment by supporting other team members, mentoring junior data scientists, and contributing to Elsevier’s diversity, equity, and inclusion practices.
  • Fostering data science literacy and community, giving internal presentations and demos, and representing Elsevier data science externally at conferences and with publications


Qualifications

  • Advanced degree (PhD or MSc and equivalent experience) in Data Science, Computer Science, Statistics, Mathematics, Linguistics, or a related discipline
  • Demonstrated ability to independently plan, lead, & execute complex Data Science projects.
  • Ability to work across coding languages used in Data Science (e.g. Python, SQL) to perform advanced analyses (such as algorithm development, statistical analyses or training predictive models) as well as manipulating and visualizing data.
  • Experience working on commercial data science projects with data engineering and software engineering principles in putting data science into production.
  • Thrives on working collaboratively and enjoy supporting, learning from, and sharing knowledge with a team of people from different backgrounds.

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