Bioptimus

• Data Partnership Operations & Lifecycle Management: Own the operational lifecycle of external data partnerships following contract signature. Act as the primary operational and technical point of contact for hospitals, biobanks, CROs, and research laboratories. Coordinate onboarding, data delivery timelines, and stakeholder communication to ensure successful execution of partnership milestones. • Data Transfer & Infrastructure Coordination: Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. Coordinate access management, encryption, and ingestion workflows across cloud storage systems (AWS S3, SFTP, APIs, direct upload pipelines). Ensure incoming datasets are delivered, validated, and tracked according to internal governance standards. • Clinical & Multi-Omics Data Harmonization: Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards across complex clinical and multi-modal datasets. Organize and maintain relationships between clinical metadata and associated omics or imaging assets, including genomics, transcriptomics, spatial biology, and pathology data. • Pipeline Operations & Automation: Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. Identify operational bottlenecks and repetitive workflows and convert them into scalable systems, scripts, templates, dashboards, or automation tools that improve operational efficiency and visibility. • Data Quality Oversight: Coordinate automated and manual quality control checks across incoming datasets. Identify missing data, inconsistencies, corruption, or metadata mismatches and work directly with external partners to resolve issues. Ensure data integrity, traceability, and version control throughout the ingestion process. • Operational Tracking & Reporting: Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking. Build and maintain reporting dashboards and operational tools to provide visibility into project progress, ingestion velocity, and operational risks. • Cross-Functional Collaboration & Communication: Partner closely with Data Science, Engineering, Legal, and Partnership teams to align operational execution with business and scientific priorities. Communicate technical issues clearly to both scientific collaborators and non-technical stakeholders. Provide regular updates on operational risks, blockers, and delivery progress. • Site Visits & External Partner Engagement: Conduct periodic visits to partner hospitals, biobanks, and laboratories to support onboarding, troubleshoot technical or operational bottlenecks, and strengthen long-term collaborations.

• Own the operational lifecycle of external data partnerships following contract signature. • Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. • Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards across complex clinical and multi-modal datasets. • Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. • Coordinate automated and manual quality control checks across incoming datasets. • Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking. • Partner closely with Data Science, Engineering, Legal, and Partnership teams to align operational execution with business and scientific priorities. • Conduct periodic visits to partner hospitals, biobanks, and laboratories to support onboarding, troubleshoot technical or operational bottlenecks, and strengthen long-term collaborations.

• Own the operational lifecycle of external data partnerships following contract signature. Act as the primary operational and technical point of contact for hospitals, biobanks, CROs, and research laboratories. • Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. Coordinate access management, encryption, and ingestion workflows across cloud storage systems (AWS S3, SFTP, APIs, direct upload pipelines). • Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards across complex clinical and multi-modal datasets. • Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. Identify operational bottlenecks and repetitive workflows and convert them into scalable systems, scripts, templates, dashboards, or automation tools that improve operational efficiency and visibility. • Coordinate automated and manual quality control checks across incoming datasets. Identify missing data, inconsistencies, corruption, or metadata mismatches and work directly with external partners to resolve issues. • Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking.

• Own the operational lifecycle of external data partnerships following contract signature. • Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. • Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards. • Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. • Identify missing data, inconsistencies, corruption, or metadata mismatches and work directly with external partners to resolve issues. • Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking. • Partner closely with Data Science, Engineering, Legal, and Partnership teams to align operational execution with business and scientific priorities. • Conduct periodic visits to partner hospitals, biobanks, and laboratories to support onboarding.

• Influence the go-to-market strategy: Analyze & identify the optimal positioning to successfully deploy our science, models and products. • Develop Strategic Partnerships: Identify, negotiate, and manage strategic partnerships with key stakeholders in biopharma. • Revenue Growth and Target Achievement: Drive revenue growth by meeting or exceeding business development targets. Monitor and analyze sales performance metrics to inform strategic decisions. • Stakeholder Engagement: Actively promote Bioptimus at industry events, conferences, and through digital platforms. Develop & deliver compelling pitches and presentations to engage potential clients and partners. Maintain strong relationships with existing partners and stakeholders while continually expanding the company’s network. • Teamwork and Collaboration: Collaborate with cross-functional teams, including R&D, marketing, and product development, to ensure alignment of business strategies and goals.

• Develop and implement comprehensive data validation protocols for diverse biological datasets (histology, omics, clinical). • Establish and enforce data standardization practices to facilitate seamless integration and analysis across different data types. • Work closely with the R&D team to understand data requirements and address data quality concerns. • Maintain a detailed documentation of the data-quality assessment procedures, validation results, and data specifications. • Evaluate and validate external public data sources, ensuring they meet quality standards. • Stay up-to-date with the latest data quality best practices and tools in the biological domain.

• Data Validation Pipeline Development: Develop and implement comprehensive data validation protocols for diverse biological datasets (histology, omics, clinical). Ensure data integrity, consistency, and accuracy through rigorous quality checks. Design and implement automated data quality pipelines to streamline data validation and identify potential issues early in the data processing workflow. • Data Curation & Standardization: Establish and enforce data standardization practices to facilitate seamless integration and analysis across different data types. Curate datasets to enhance their usability for machine learning. • Collaboration & Communication: Work closely with the R&D team to understand data requirements and address data quality concerns. Communicate data quality findings and recommendations effectively to technical and non-technical stakeholders. Communicate and synchronize with external data providers. • Documentation & Reporting: Maintain a detailed documentation of the data-quality assessment procedures, validation results, and data specifications. Generate regular reports on data quality metrics and trends. • Data Source Evaluation: Evaluate and validate external public data sources, ensuring they meet our quality standards and are suitable for inclusion in our foundation model training. • Continuous Improvement: Stay up-to-date with the latest data quality best practices and tools in the biological domain. Propose and implement improvements to our data- quality assessment processes and pipelines.

• Develop and implement comprehensive data validation protocols for diverse biological datasets (histology, omics, clinical). • Establish and enforce data standardization practices to facilitate seamless integration and analysis across different data types. • Work closely with the R&D team to understand data requirements and address data quality concerns. • Maintain a detailed documentation of the data-quality assessment procedures, validation results, and data specifications. • Evaluate and validate external public data sources, ensuring they meet our quality standards.

• Architect, build, and deploy cross-functional agentic AI workflows. • Partner with business stakeholders to understand operational problems and translate them into technical designs. • Implement solutions using LLMs, agents, RAG, orchestration, and MLOps best practices. • Report to the COO and collaborate with Engineering, Operations, and cross-functional teams.