AI use among life, physical, and social science technicians, all other is little observed so far. Measured exposure for life, physical, and social science technicians, all other: 10% (little observed).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

About TetraScience

TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.

In connection with your candidacy, you will be asked to carefully review “ The Tetra Way ,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.

As a Scientific Data Architect supporting our partnership with a top 25 global biopharma manufacturing and quality organization, you will design the scientific data models and use cases that connect manufacturing processes, laboratory results, and product quality across manufacturing sites and external partners. Your work will support investigations, continued process verification (CPV), process improvement, technology transfer, and the application of AI to biologics manufacturing.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.

Candidates will be asked to review “The Tetra Way” as part of the interview process.

Who You Are

You combine deep experience in biologics manufacturing and quality control with the technical ability to design, prototype, and implement scientific data solutions. You understand how a manufacturing process generates data, how QC laboratories test for product quality, and how to preserve the relationships between those records.

You are comfortable working directly with manufacturing scientists, Manufacturing Sciences and Technology (MSAT), manufacturing IT, QC, Quality Assurance, data engineers, and product teams. You translate their questions into explicit data requirements, reusable models, and applications they can use in daily work.

You take ownership from initial site discovery through implementation and adoption. You write code, examine source data, test assumptions with scientific users, and demonstrate working solutions.

You have the willingness and ability to travel up to 50% of working time between partner manufacturing sites for discovery, implementation, workshops, training, and adoption support.

Requirements

What You Have Done

What you must bring

A minimum of 3-5 years direct experience in biologic drug manufacturing and QC is required, including substantial experience with monoclonal antibodies or other recombinant therapeutic proteins. You have designed data models and delivered analytical or digital use cases using manufacturing and QC data for these modalities.

Strong Python and SQL skills, with experience developing parsers, transformations, APIs, and analytical applications using tabular and JSON data.

Ability to communicate architecture decisions and scientific findings to laboratory users, manufacturing teams, engineers, and business leaders.

Desired: Manufacturing & Quality

Relevant life sciences industry experience, with substantial work in biologics manufacturing, process development, MSAT, analytical development, or QC.

Demonstrated understanding of upstream and downstream biologics operations, including cell culture, harvest, chromatography, filtration, and drug-substance production, and how process conditions relate to yield and quality.

Experience defining and implementing manufacturing or QC use cases such as batch investigations, continued process verification (CPV), yield analysis, raw-material variability assessment, stability trending, or technology transfer.

Experience delivering solutions in GxP environments, including traceability, data integrity, controlled changes, reproducible calculations, and collaboration with Quality and Validation teams.

Desired: Data & Technology

A proven record of designing conceptual, logical, and physical data models that connect products, batches, material lots, process steps, equipment, samples, analytical results, and quality events across systems.

Experience integrating scientific and manufacturing data from systems such as LIMS, laboratory instruments, process historians, MES, ERP, ELN, and quality management systems.

What You Will Do

Together with the Scientific Business Analyst (SBA), lead onsite discovery with manufacturing, MSAT, QC, IT, and Quality teams. Map scientific workflows, inspect representative source data, identify gaps, and define prioritized use cases with measurable outcomes and acceptance criteria.

Together with the platform knowledge engineering team (semantic management, data modeling, ontology), design reusable product, process, and QC data models for biologics manufacturing. Establish shared structures and controlled vocabularies while preserving product-, modality-, and site-specific requirements.

Model batch and material genealogy, including splits, pools, intermediate lots, and links between drug substance and drug product. Connect process measurements and QC results to the correct batch, unit operation, sample, method version, and specification.

Define mappings across sites and external manufacturing partners, reconciling identifiers, terminology, units, timestamps, and process hierarchies while retaining source provenance.

Build use cases for investigation evidence assembly, CPV, cross-batch comparisons, process and yield analysis, and technology transfer. Translate scientific questions into data requirements, calculations, prototypes, and production-ready specifications.

Develop Python parsers, data transformations, quality checks, and applications using Tetra OS. Collaborate with engineering teams to deliver reliable integrations and maintainable data products.

Prepare contextualized datasets for statistical, mechanistic, and AI/ML models. Work with scientific users and applied AI engineers to assess data suitability, interpret outputs, and evaluate performance.

Support verification and validation through documented requirements, source-to-target mappings, test evidence, and reproducible analyses, working within partner Quality processes.

Demonstrate solutions regularly, incorporate user feedback, and train partner teams to maintain and extend delivered models and applications.

Multi-site manufacturing data harmonization and external CMO/CDMO data integration.

Process historian and time-series data, chromatography data, statistical process monitoring, and multivariate analysis.

Databricks, cloud data platforms, and manufacturing information models such as ICH, ISA-88 and ISA-95.

Benefits

100% employer-paid benefits for all eligible employees and immediate family members

Unlimited paid time off (PTO)

401K

Company paid Life Insurance, LTD/STD

A culture of continuous improvement where you can grow your career and get coaching

We are not currently providing visa sponsorship for this position

The salary range for this position is $140,000 - $240,000. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.