Senior Biotech Data Scientist & Data Infrastructure Engineer

Amplitude Venture Capital; United States; On-site; USD 150,000 - 225,000; Full time

Job summary

Nomic is seeking a senior data-centric individual to design and automate data pipelines for nELISA data. You will bridge lab science and data infrastructure, building robust tools that empower scientists to extract insights from profiling and QC data at scale.

You will contribute across the data stack, from experimental datasets to internal analytics, collaborating with data engineers, software engineers, and R&D teams to accelerate discovery and improve workflows in a fast-paced biotech

Qualifications

• Graduate degree or equivalent industry experience in biosciences with a focus on biosensors, fluorescence data, or similar.
• 3+ years of experience analyzing bioscience data and developing data processing algorithms.
• 2+ years software engineering/development experience; able to stand up tools for non-programming users and collaborate with engineers.

Responsibilities

• Design, build, automate data pipelines and algorithms to process raw flow cytometry data into quantitative protein measurements in collaboration with data, software, and lab teams.
• Serve as data interpretation expert for nELISA datasets, linking science to data features and anomalies.
• Develop data support features and new pipelines for QC and profiling data; ensure robust tooling across R&D and operations.
• Communicate effectively, work in a team, and troubleshoot issues related to nELISA data and tooling.
• When tools don’t exist, develop new data analysis pipelines leveraging bioscience expertise.

Skills

Biosensors and fluorescence data

Data analysis

Software development

Communication

English fluency

Education

Graduate Degree in bioengineering or related biosciences

Tools

Python

SQL

Job description

Nomic is seeking a senior data-centric individual to design and automate data pipelines for nELISA data. You will bridge lab science and data infrastructure, building robust tools that empower scientists to extract insights from profiling and QC data at scale.

You will contribute across the data stack, from experimental datasets to internal analytics, collaborating with data engineers, software engineers, and R&D teams to accelerate discovery and improve workflows in a fast-paced biotech