Scientific Data Engineer

United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Scientific Data, AI Cloud, Biotechnology, PharmaceuticalsIndustries

Requirements

Candidates must possess over 3 years of experience in Python and SQL, hold a PhD in biology, chemistry, or a related field, and have extensive wet lab experience, particularly with HPLC and Mass Spec. Experience with specific LC instruments from Waters, Thermo Fisher, Cytiva, Shimadzu, Sciex, or Agilent is required. Familiarity with data plotting and dashboarding tools like Streamlit, Tableau, or Jupyter Notebook is a plus. Excellent communication, attention to detail, proactive problem-solving, and the ability to manage multiple projects simultaneously are essential. Intellectual curiosity and a creative approach to problem-solving are highly valued.

Responsibilities

The Scientific Data Engineer will research data acquisition strategies for scientific lab instrumentation and develop parsers for various instrument output files. They will design and build data models, pipelines, unit tests, integration tests, and reusable utility functions. Responsibilities include cross-analyzing instrument data to design common data model components and building visualizations, reports, and dashboards. The role involves driving customer value by ensuring solutions meet requirements and providing feedback to product management and engineering.

Skills

Data Engineering

Data Modeling

Data Pipelines

File Parsers

Streamlit

Tableau

Jupyter Notebook

Scientific Data

TetraScience

Cloud platform for scientific data management

About TetraScience

TetraScience offers a cloud-based platform called the Scientific Data Cloud, which helps biopharmaceutical companies manage and harmonize their scientific data for research and development, quality assurance, and manufacturing. The platform connects various lab instruments and software, streamlining data management and significantly reducing task completion time. TetraScience's vendor-neutral and open design allows it to work with any lab equipment, making it a flexible solution in the life sciences sector. The company's goal is to enhance scientific outcomes by preparing data for artificial intelligence and machine learning applications.

Boston, MassachusettsHeadquarters

2019Year Founded

$113.8MTotal Funding

SERIES_BCompany Stage

AI & Machine Learning, Biotechnology, HealthcareIndustries

51-200Employees

Benefits

Unlimited PTO

100% company paid health, dental, & vision

Company paid life insurance

401k savings

Company paid disability insurance

Equity program

Flexible work arrangements

Risks

Rapid AI development may outpace TetraScience's integration capabilities, risking obsolescence.

Dependency on partners like Google Cloud and NVIDIA could pose risks if disrupted.

International expansion may expose TetraScience to regulatory and compliance challenges.

Differentiation

TetraScience offers a vendor-neutral, open, cloud-native platform for scientific data management.

The platform integrates with any lab equipment or software, enhancing flexibility and adaptability.

TetraScience's Scientific Data Cloud centralizes and harmonizes data, preparing it for AI/ML applications.

Upsides

Partnerships with NVIDIA and Google Cloud enhance AI-native scientific datasets and capabilities.

Collaboration with Databricks accelerates the Scientific AI revolution in life sciences.

Bayer AG partnership maximizes scientific data value, driving innovation in biopharma.

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