Data Operations Lead

Bioptimus

Data Operations Lead managing biomedical data partnerships for AI foundation models at Bioptimus. Overseeing data transfers, ensuring quality, and collaborating with various stakeholders in the biotech field.

Posted 5/26/2026full-timeRemote • 🇫🇷 FranceSeniorWebsite

Tech Stack

Tools & technologies

AWSCloudPythonSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

Biomedical Data Expertise: Strong understanding of clinical and biomedical data structures, including real-world data, clinical trial datasets, and multi-omics data modalities. Familiarity with oncology, immunology, or related therapeutic areas is highly desirable.
Cloud & Data Infrastructure: Proven experience managing data lifecycles in cloud environments, particularly AWS (S3, CLI, access management). Familiarity with secure data transfer protocols and large-scale biomedical data handling workflows.
Data Wrangling & Technical Skills: Proficiency in Python or R, along with SQL for querying and transforming datasets. Ability to write lightweight scripts, automate workflows, and interact with APIs or cloud-based systems.
Project & Stakeholder Management: Demonstrated ability to manage multiple external collaborations and operational workstreams simultaneously. Excellent communication skills, with the ability to translate technical issues into clear guidance for both scientific and non-technical stakeholders.
Operational Problem Solving: Comfortable working independently in ambiguous environments. Strong analytical and organizational skills with the ability to identify bottlenecks, improve processes, and drive operational efficiency.
Educational Background: Bachelor’s or Master’s degree in Life Sciences, Bioinformatics, Health Informatics, Computer Science, or a related quantitative field.

Benefits

Comp & perks

A collaborative and mission-driven work environment.
Competitive salary and equity package.
Flexible work arrangements, including remote options.
Opportunities for professional growth and leadership development.
The opportunity to shape the future of biology and AI through groundbreaking work.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonRSQLdata wranglingdata lifecycle managementcloud infrastructuredata transfer protocolsquality controlautomationdata modeling

Soft Skills

project managementstakeholder managementcommunicationanalytical skillsorganizational skillsproblem solvingcollaborationindependenceprocess improvementtechnical guidance