Company Name:-
Novome Biotechnologies

Job Location:-
South San Francisco, CA 94080

Job Summary:-
Job detailsJob TypeFull-timeFull Job DescriptionSenior Research Associate / Scientist – Data Analytics
Novome is building genetically engineered microbial medicines (GEMMs) that perform defined activities in the human gut to treat chronic diseases.

We have developed the first platform for controlled colonization of the gut with engineered therapeutic bacteria, and are applying this to multiple disease areas as well as expanding our platform capabilities.

We are moving our lead program in secondary hyperoxaluria into Phase 1 clinical trials, and are actively progressing programs in IBS, IBD, and Immuno-oncology.

We are seeking a SRA/Scientist to join our Informatics Team.

Our team is dedicated to building internal tools for our research staff to facilitate the groundbreaking science they do.

Our ideal candidate is someone who is excited to support researchers in analyzing their complex experimental data and developing automated data analysis pipelines.

When you join Novome, you can expect to:
Collaborate with lab staff to draw insights from experimental data ranging from proof-of-concept in vitro experiments to our preclinical studies in animal models.

Contribute to the development of our internal data analysis software package, which automates common analyses to improve accessibility and promote standardization across the company.

Evaluate researchers’ analysis needs and help determine the services and tools the Informatics Team provides.

Receive direct feedback from your users and teammates on the impact and quality of your work.

Enjoy a healthy work-life balance.

We may be a small team, but we’re not crazy.

What you bring to Novome:
A degree in bioengineering, molecular/cell biology, biochemistry, microbiology, bioinformatics, or a related discipline
A PhD, MS + 3 years, or BS + 5 years professional experience working in a biology lab or with lab researchers
Fluency in Python is required
Proficiency with data science libraries such as Pandas, NumPy, and SciPy and visualization libraries such as Seaborn, Plotly, Matplotlib, and Bokeh is a strong plus
Experience applying statistical concepts (e.

g.

, hypothesis testing, probability distributions, confidence intervals, etc.

) to biological data, and familiarity wit

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