Scientific Knowledge Engineer - Ontology

Immediate need for a Scientific Knowledge Engineer to support data use in the biotechnology research space. As a Scientific Knowledge Engineer, you will work with Product Managers and R&D SME's using ontology to organize data models, using data, knowledge, and prediction to find new medicines.

Schedule/Shift: 1st shift, Mon.-Fri.

Position Type: Contract role for 1 yr.

Position Summary: This role is responsible for maximizing the value of our data assets over a lifetime to bring purpose to data by acting as translators of highly technical information from domain experts into an appropriate data model – complete with significant ontology and vocabulary - that can be utilized to effectively structure and index the data. Specifically working with Product managers and R&D subject matter expertise to define the language (data models, ontology, standards, etc.) of science into data products by acting as the voice of “Knowledgebase” and interoperability/value of asset. This includes responsibility for the understanding and translation of computational methods back through the data chain to maximize the quality and speed of data from source to drive experimental multi-variant analysis and data driven decision-making.

Essential Duties and Responsibilities:

Definition of schemas and data models of scientific information required for the creation of value adding data products. This includes accountability for the quality control and mapping specifications to be industrialized by data engineering and maintained in platform provisioned tooling.

Accountable for the quality control (through validation and verification) of mapping specifications to be industrialized by data engineering and maintained in platform provisioned tooling – e.g., models, schemas, controlled vocab.

Working with Product managers/engineers confidently convert business need into defined deliverable business requirements to enable the integration of large-scale biology data to predict, model, and stabilize therapeutically relevant protein complex and antigen conformations for drug and vaccine discovery.

Collaborate with external groups to align data standards with industry/ academic ontologies ensuring that data standards are defined with usage / analytics in mind. May also provide data source profiling and advisory consultancy to R&D outside of suite of next generation platforms.

Support effective ingestion of data by company through understanding the entry requirements required by platform engineering teams and ensuring that the “barrier for entry” is met e.g. Scientific information has the appropriate metadata to be indexed, structured, integrated and standardized as needed. This may require articulation of company engineering standards and metadata information needs to third parties to ensure efficient and automate ingestion at scale.

Provides bespoke subject matter expertise for R&D data to translate deep science into data for actionable insights

Qualifications:

Bachelor’s degree (BS) in Bioinformatics, Biomedical Science, Biomedical Engineering, Molecular Biology, or Computer Science.

Biologist related work experience

Five (5) - eight (8) years of relevant, industry experience with an established track record of delivery

Working experience querying relational databases - SQL

Experience with industry standard data management / metadata platforms e.g. Collibra, Datahub, Datum, Informatica

Data modeling, quality, analysis, profiling (working experience with any data quality tool, SAS, Ataccama, Informatica Data Quality, Talend, OpenRefine)

Experience with industry standard tools for building data protocols e.g. Avro, Protocol Buffers, Thrift

Experience with at least one programming language – e.g. Python – for scripting vocabulary mappings, building data models, etc.

Awareness of RDF, Ontology, reference data

Preferred Skills/Experience:

Membership of data standards group, industry committee, board, or consortium

Specific experience with ontology, knowledge Graph efforts

Experience in technical writing, documentation