Data Scientist, Valo Health

• Degree in a scientific field with 4+ (BS), 2+ (MS), or 0+ (PhD) years of experience
• Demonstrated ability to communicate with cross-functional stakeholders, including technical and non-technical audiences
• Experience with small molecule assay data (biochemical, cellular, ADME, PK, cytotoxicity)
• Demonstrated ability to clean, curate, and harmonize semi-structured data with SQL and python, with an eye towards generalizability and code re-usability
• A strong understanding of common ontologies, controlled vocabularies, knowledge bases, and databases related to drug discovery data (BioAssay ontology, NCBI taxonomy, HGNC, Uniprot, ChEBI, ChEMBL)
• Experience balancing tradeoffs between perfect and sufficient. Able to establish acceptable error rates, estimate expected errors in data, and communicate those expectations to broad audiences

Desirable

• As a Data Scientist II, Scientific Data Curation, you will collaborate with stakeholders across data science, data engineering, lab informatics, medicinal chemistry, and assay development to establish high quality, well-structured datasets for use within the Opal Computational Platform.
• These datasets will include biochemical, cellular, ADME, PK, cytotoxicity, and other assays related to drug discovery.
• We are looking for someone who is passionate about data, curious to explore the complexities of semi-structured datasets, and takes an intuitively quantitative approach to data exploration and curation.
• This role will include exploratory analysis of data, building scalable data pipelines to clean and harmonize data, and generation of visualizations, dashboards, and written communications to empower scientists across Valo to utilize large scale data as part of the Opal Platform
• Develop standard procedures for establishing small molecule assay datasets used for modeling, including data cleaning, labeling, harmonizing, reporting, and QC
• Explore and evaluate new datasets to prioritize use at Valo
• Collaborate with relevant stakeholders to establish high quality, curated datasets for modeling
• Contribute to development and refinement of data standards and data models, and establish consistent internal terminologies and ontologies