SciPro Inc.
SciPro has partnered with a rapidly growing, cloud-native healthtech company transforming how life sciences organizations structure and activate their data. With deep integration across the worlds leading cloud and AI platforms, this company is redefining how R&D data is collected, modeled, and leveraged to accelerate discovery.
We are seeking a
Senior Data Architect
to bridge the gap between complex scientific workflows and scalable digital solutions. In this role, you will collaborate with scientific stakeholders and cross-functional teams to design robust data architectures, enable AI/ML readiness, and deliver solutions that empower the biopharma and health industries to accelerate innovation. This role is
based in Boston
(hybrid: primarily remote, but must be available for onsite client engagements as needed). Key Responsibilities Translate scientific and lab-based workflows into scalable, data-driven solutions Architect and implement structured data models (SQL, NoSQL, JSON schemas, ontologies) tailored for scientific data Develop Python-based pipelines for parsing, modeling, and integrating scientific datasets Integrate lab systems (ELNs, LIMS, and other software) via APIs to support seamless workflows Collaborate with customer stakeholders to gather requirements and deliver fit-for-purpose solutions Present technical solutions and progress updates to both scientific and non-technical audiences Identify reusable components to inform platform productization and future builds Serve as a trusted technical and scientific partner to clients, driving long-term adoption and success Core Qualifications PhD with 7+ years or Masters with 10+ years of experience in
Python (scale 89/10 coding ability)
for data architecture, engineering, or scientific applications Strong expertise in
data modeling
(SQL, NoSQL, ontologies) Experience integrating lab systems (ELNs, LIMS, APIs) Familiarity with cloud platforms ( AWS required , Azure/GCP a plus) Background in
life sciences
(biotech, pharma, health & wellness, or adjacent) experience in a scientific setting is required Experience in drug discovery, preclinical development, or CMC. Strong problem-solving and communication skills; ability to engage with scientists and stakeholders at various technical levels Customer-facing experience and comfort leading discussions with external partners
Senior Data Architect
to bridge the gap between complex scientific workflows and scalable digital solutions. In this role, you will collaborate with scientific stakeholders and cross-functional teams to design robust data architectures, enable AI/ML readiness, and deliver solutions that empower the biopharma and health industries to accelerate innovation. This role is
based in Boston
(hybrid: primarily remote, but must be available for onsite client engagements as needed). Key Responsibilities Translate scientific and lab-based workflows into scalable, data-driven solutions Architect and implement structured data models (SQL, NoSQL, JSON schemas, ontologies) tailored for scientific data Develop Python-based pipelines for parsing, modeling, and integrating scientific datasets Integrate lab systems (ELNs, LIMS, and other software) via APIs to support seamless workflows Collaborate with customer stakeholders to gather requirements and deliver fit-for-purpose solutions Present technical solutions and progress updates to both scientific and non-technical audiences Identify reusable components to inform platform productization and future builds Serve as a trusted technical and scientific partner to clients, driving long-term adoption and success Core Qualifications PhD with 7+ years or Masters with 10+ years of experience in
Python (scale 89/10 coding ability)
for data architecture, engineering, or scientific applications Strong expertise in
data modeling
(SQL, NoSQL, ontologies) Experience integrating lab systems (ELNs, LIMS, APIs) Familiarity with cloud platforms ( AWS required , Azure/GCP a plus) Background in
life sciences
(biotech, pharma, health & wellness, or adjacent) experience in a scientific setting is required Experience in drug discovery, preclinical development, or CMC. Strong problem-solving and communication skills; ability to engage with scientists and stakeholders at various technical levels Customer-facing experience and comfort leading discussions with external partners