Scrum Master – Data & Analytics (AI/ML Ops)
About The Position
Our client, a pioneering biotechnology company, is seeking a dedicated Scrum Master â Data & Analytics to enable high-performing Agile delivery with a specific focus on implementing AI/ML Ops practices. This role is central to the organizationâs ambition to become a fully integrated biotech company by delivering governed, reliable, and scalable AI/ML solutions. You will serve as a "Player-Coach," partnering with Product Owners, technical leads, and vendors to manage a portfolio that includes data platforms, ML models, AI agents, and automation workflows. This role requires onsite preserence in Carlsbad, CA 3 days a week (Tues-Thursday).
Requirements
- Bachelorâs degree in Computer Science, Engineering, Information Systems, or a related field (Advanced degree or certifications like CSM, PMI-ACP, or SAFe preferred).
- 5â8+ years in Agile delivery roles supporting data, analytics, or software engineering teams.
- Hands-on experience implementing DevOps or MLOps practices (CI/CD pipelines, observability, incident response) for AI/ML workloads.
- Working knowledge of modern architectures (Lakehouse, vector search, semantic layers) and tools such as Databricks and Jira.
- A track record of enabling teams to self-organize and continuously improve.
- Ability to simplify complex technical topics and align diverse stakeholders (R&D, Clinical, Commercial) on priorities.
- A strong commitment to the ethical, responsible, and compliant use of data and AI.
Nice To Haves
- Experience in Life Sciences, Healthcare, or other regulated industries is strongly preferred.
Responsibilities
- Serve as the Scrum Master for one or more Data & Analytics squads delivering use cases such as patient finding, trial optimization, and AI-assisted authoring.
- Facilitate all core Agile ceremonies (sprint planning, daily stand-ups, retrospectives) ensuring they are purposeful and outcome-oriented.
- Remove delivery impediments by coordinating across IT, security, and business partners.
- Drive the adoption of AI/ML Ops practices, including version control, CI/CD for data and models, automated testing, and monitoring.
- Partner with architecture leads to align team practices with modern data platform patterns (e.g., Databricks Lakehouse, RAG, agentic automation).
- Ensure "Definition of Done" includes operationalization requirements like observability, lineage, and documentation rather than just the initial build.
- Coach Product Owners, Engineers, and Analysts on Agile principles, story writing, and flow-based metrics (throughput, cycle time, WIP).
- Promote consistent use of IT Product Centric Framework (ITPCF) patterns and Jira standards.
- Foster a culture of psychological safety, turning retrospective insights into concrete action items.
- Ensure delivery practices support regulatory expectations (GxP/SOX) for data and AI in a life sciences context, including validation and audit trails.
- Partner with Data Governance and Security to embed required controls (policy-as-code) directly into team workflows.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
1-10 employees
