Scientific Business Analyst- Boston or Bay Area

Boston, Massachusetts, 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, Drug Discovery, Preclinical Development, CMC, QualityIndustries

Requirements

Candidates should possess a PhD with over 15 years of industry experience in life sciences, preferably in pharma, biotech, or health tech, with deep domain expertise in discovery, preclinical, CMC, and/or Quality. Extensive hands-on experience or direct oversight in areas such as high throughput screening, preclinical toxicology, materials engineering, analytical development, or drug substance synthesis and manufacturing is required. Proven experience in delivering requirements for AI/ML-driven solutions that improved efficiency, reduced cost, and enhanced data utilization is necessary. A strong coding or scripting background (e.g., Python, Nextflow, AWS, SDKs) and familiarity with scientific tools, databases, and ontologies are preferred. Exceptional communication and storytelling skills to engage technical and executive stakeholders, along with prior experience in customer-facing, consulting, or commercial-scientific interface roles, are essential.

Responsibilities

The Scientific Business Analyst will collaborate with scientists, product managers, and engineers to transform complex scientific data into actionable outcomes, focusing on uncovering innovative use cases for AI and machine learning applications. They will develop requirements for complex solutions targeted at R&D and Quality personas within Life Sciences, embodying extreme ownership and deriving maximum value from data through enrichment, analysis, and integration with AI and machine learning applications. This role involves regularly working onsite with customers in dynamic, high-impact, face-to-face collaborative environments to build deep relationships and drive scientific transformation.

Skills

Scientific Data

Machine Learning

Drug Discovery

Preclinical Development

CMC

Quality

Data Analysis

Business Analysis

Product Management

Scientific Use Cases

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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