San Francisco, CA, US

We are seeking a highly skilled and motivated Scientist to lead and support implementing data assessment and decision systems for biological assays: High-throughput Screening (HTS), in vitro and cellular, in vitro and in vivo ADME and toxicology. This person will establish and support the models and processes used to assess the quality of data, the statistical methods to apply, the criteria for selection of hits, and the capture of decisions why particular methods were selected. This individual will work closely with a team of interdisciplinary scientist and would be providing insights to other scientist to leverage scientific and information systems to record and drive the decisions of the teams. The ideal candidate will be self-driven, resourceful, very organized and focused, and enjoy working in a dynamic team environment.

Primary Functions

Understand raw data from low-throughput in vitro and high-throughput screening (HTS) assays
Create automated scripts to process assay data into analysis-ready results including consideration to outliers, edge effects, controls, and methods to calculate IC50 and EC50
Assist scientists with statistical analysis and interpretation of results into decisions, including pass/fail of target hit, compound confirmation, and activities in counter screens
Develop, refine, and deploy models to visualize single and multiple data sets in order to streamline the decision-making process with scientists and leaders
Present meaningful, easy-to-understand, consistent visualizations incorporating statistical assessment of data quality with comparisons among methods, data sets, and assay conditions
Analyze historical trends across targets and share insight into classes of action and promiscuity
Discuss/Document workflow and requirements of scientists into action items
Assist in the proper storage and retrieval of information to/from databases

Ph.D. in Computational Biology, Computer Science, Bioinformatics, Statistics, or related field with 3+ year’s post-degree experience. Master’s degree candidate with 8+ years’ experience would also be considered.
Experience with multivariate statistical techniques, algorithms, and packages to handle systematic and random error in multiple types of biological experiments and implement them in data analysis workflow
Knowledge of appropriate models to filter and fit multi-dimensional, chemical and assay data as well as assess the quality of the data being reviewed
Expertise in comprehensive data analysis with commercial scripting tools such as Pipeline Pilot and decision tools such as Dotmatics’ Vortex and TIBCO Spotfire
Ability to use statistical packages such as R, JMP, Minitab, and ProMV
Ability to code algorithms using formal (e.g., Java) or scripting (e.g., Python) languages
The successful candidate will be aligned to Nurix’ culture and values; he/she will be team oriented and highly collaborative with a hands-on approach
The candidate should be enthusiastic, driven, have the ability to work independently and thrive in a dynamic start-up environment