Senior FAIR Data Scientist (all genders) - (unlimited / fulltime)

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

AbbVie’s Information Research (IR) group has a mission to unlock information that makes cures possible. Within IR, the AbbVie Library Information Science (LIS) team builds tools and provides services that help AbbVie scientists interact with, manage, and consume knowledge from scientific literature to foster innovation in drug discovery and development. LIS is a global organization, with a large presence in the Lake County headquarters as well as other research sites. We leverage leading technologies and methods to provide AbbVie scientists with our industry’s best capabilities around literature-based knowledge discovery. One important aspect is to make these tools, data sources, and the derived knowledge FAIR (findable, accessible, interoperable, and reusable), following industry best practices.

This FAIR Data Scientist role will be part of the Literature-based scientific discovery (LSD) team in the Library Information Science group. The FAIR Data Scientist should have a sufficiently strong scientific and technical background to scope and fulfill LSD data modeling and convergence services. LSD services include knowledge and data extraction, ontology-enabled text and data mining from scientific literature, patents, and other information resources, as well as semantic enrichment, visualization, and library information search services. Additionally, the FAIR Data Scientist will effectively collaborate across Library Information Science and other IR teams to deliver LSD services to our clients. On a higher level, the role will support ongoing large-scale data convergence initiatives by working with individual clients on various data FAIRification projects which feed into a central convergence hub solution.
 

• Leverage scientific domain and technical knowledge to support AbbVie scientists with making their unstructured and structured data assets FAIR and supporting ongoing data convergence initiatives across AbbVie businesses.
• Tech-savvy user-lead for customized FAIR data tools, working with business clients on developing, applying, and curating FAIR ontologies, semantic models, and related services.
• Collaborate with internal data scientists, information scientists and software developers to design and implement LSD-tailored solutions that follow FAIR data principles and meet well-defined stakeholder requirements.
• Be a trusted partner of AbbVie’s scientific communities in Discovery and Development Science projects.
• Use ontology-based text data mining, scientific data analytics, APIs, and other tools to drive end-user engagement, improved R&D efficiencies, and reveal new discovery opportunities across therapeutic focus areas.
• Provide unique scientific insights and expertise by establishing, streamlining, and automating a knowledge extraction pipeline for collecting, extracting, curating, and visualizing knowledge from scientific publications including statistical context.
• Consult on knowledge graph projects and tools to visualize knowledge from scientific literature and participate in defining requirements, system architecture, and support implementation.
• Monitor new technology trends related to modeling, knowledge discovery, digitalization and convergence of FAIR semantic data. Establish an external presence in precompetitive projects as well as through conferences and publications.
• Achieve great results while overwhelmingly demonstrating key AbbVie values and behaviors.

Qualifications:

• Master’s degree (+8 years of experience) or Ph.D. degree (+2-4 years of experience) in Bioinformatics/ Computational Biology, Cheminformatics, or life sciences (Biology, Pharmacy, Medicine, Pharmacology, Biochemistry, Medicinal Chemistry).
• Solid biomedical knowledge and ability to translate complex scientific questions from research scientists and business partners into ontologies, semantic models, and related information solutions.
• Good understanding of the FAIR Data principles in the biomedical domain. Practical experience with FAIRification processes is a plus.
• Experience in developing, implementing, and maintaining large, complex taxonomies, ontologies, and knowledge graphs (e.g. neo4j). Experience with terminology management, mapping, and classification tools.
• Excellent understanding of semantic web standards such as RDF, SKOS, OWL, OBO and experience with relational databases (SQL), triple stores (SPARQL), and/or graph databases (Cypher) are desired.
• Strong analytical skills to process, analyze, visualize, and present results. Knowledge of technologies for data analysis and visualization of complex data (e.g. Rest APIs, XML, JSON, KNIME, or Spotfire) as well as knowledge of public domain standard biomedical terminologies (e.g. MeSH, NCIt, HGNC) is desirable.
• Solid programming and informatics/data science/computer science background: Proficiency in Python required. Ability to understand and modify existing code as well as develop new scripts and set up new data processing workflows. 
• Experience with biomedical information resources (e.g. PubMed), literature and/or patent research, information analysis and data normalization would be an additional asset. Experience in text mining, natural language processing, semantic enrichment, data mining or machine learning/AI is a plus.
• Systematic problem-solving, quick learner and superior attention to detail in developing tailored solutions, high degree of reliability and integrity.
• Demonstrates an interest in working collaboratively, cross-functionally, and in inter-disciplinary teams with an ability to effectively communicate, both verbally and in writing to scientists and non-scientists.
• Well-organized and balances taking direction from others (sponsors, partners, clients) with taking initiative to manage multiple projects and learning responsibilities.
• Innate scientific curiosity, technical creativity, and innovative thinking, motivated to break new ground in the field of information/literature analysis and knowledge discovery.
• Fluency in English.