Senior Data Analyst, Clinical Monitoring & Analysis
Luna Health created Sleep-Only Automation™: insulin delivery that works while you sleep and disappears when you wake. We're a venture-backed medical device startup that makes the world's smallest insulin patch pump, designed to provide nighttime glucose control for Type 1 and Type 2 diabetes insulin users who manage their days well and have made a considered choice to stay on pens. They don't need a full replacement of their chosen therapy, they need protection while they sleep.
Our founders have lived this problem as people with diabetes and as parents of children with diabetes, and they have built some of the most recognized products in the space. Now we are building the answer to a question the industry left unanswered: what do pen users need at night that no one has built yet. Sleep-Only Automation™ is it.
The Role
We are seeking a Senior Clinical Data Analyst to own clinical trial monitoring, analysis, and reporting at Luna Health, reporting to the Director of Data Science.
This role is the clinical data heartbeat of the team. With several trials past, present, and planned, Luna needs you to monitor trial data in real time, ensure data quality, surface findings to clinical and regulatory stakeholders, and produce the analyses and reports that inform both internal decisions and external submissions. You'll work closely with our senior data scientist over the first few months to take ownership of the existing clinical reporting pipelines and dashboards (SQL, python, TypeScript, Terraform), then you will own further development and analysis.
We're looking for someone with solid clinical data experience and statistical depth, comfortable in a regulated environment, and able to communicate findings clearly to non-technical audiences. This is an execution and storytelling role that requires reliability, attention to detail, and good instincts to follow the thread through messy data.
As we approach commercial scale, this role broadens beyond clinical. The same instincts that make trial data trustworthy apply to manufacturing, quality, and business analytics, and we expect this role to grow into that scope over time.
This is an onsite/hybrid role (3+ days/week) in our San Diego office, and you must live within a reasonable commuting distance.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.
Responsibilities
Analysis and reporting
- Produce recurring and ad hoc analyses of trial data, including glycemic outcomes, device performance, safety summaries
- Write clear, accurate reports for clinical, regulatory, and leadership audiences, and contribute analytical content to regulatory submissions and publications. Support statistical analysis plan execution.
- Execute health economic outcomes research (HEOR) analyses demonstrating device value to payers
Trial monitoring and data quality
- Own and extend the clinical monitoring dashboard, including glycemic outcomes, device performance, safety signals, and data completeness. This is a custom tool built using python, SQL, TypeScript, and Terraform.
- Develop clinical monitoring tools for different user tiers, for example internal clinical/regulatory, external CRO, and external trial Site PIs or coordinators
- Own day-to-day monitoring of clinical trial data across active studies, including the trials leading to our pivotal FDA submission
- Define the checks that catch problems early. Identify, triage, and escalate anomalies, missing data, and out-of-range values with the clinical, regulatory, and engineering teams
Data and delivery
- Write and own the SQL that turns warehoused clinical data into analysis-ready datasets, and partner with our senior data engineer on what belongs in the pipeline versus the analysis layer
- Combine modern AI tools and software development best practices to build and maintain data products and tools used across the enterprise
- Keep a visible queue of analytics requests across clinical, regulatory, and leadership. Assess priority, commit to dates, and identify recurring questions as reporting or dashboarding needs.
- Maintain clear documentation of analytical methods, dataset definitions, and reporting standards
What You Bring
- 5+ years in data analysis, clinical analytics, biostatistics support, or a closely related role in medical devices, pharma, or clinical research
- Solid working knowledge of clinical trial methodology. endpoint definitions, protocol adherence, safety monitoring, database lock, and Good Clinical Practice expectations
- Demonstrated experience with clinical trial datasets, including EDC outputs and device data
- Strong SQL, including joins, aggregations, window functions, and data modeling concepts; hands-on experience with BigQuery or a comparable cloud warehouse
- Working proficiency in Python (or R) for analysis, visualization, and reporting. Work is clean, reproducible, and can be read and rerun by someone else
- Experience building and maintaining dashboards that other people depend on, and the judgment to decide what belongs on them. Current dashboards use a custom stack: python, SQL, TypeScript, and Terraform.
- A track record of owning analytical workstreams end to end under competing deadlines, including the willingness to sequence work and negotiate scope with stakeholders
- Ability to communicate analytical findings clearly, in writing and in presentation, to technical and non-technical audiences
- Attention to detail, a strong instinct for data quality, and the initiative to trace things that don't look right
- Comfort in a regulated environment, with rigid documentation and traceability requirements
Nice to Have
- Experience with diabetes, CGM, or automated insulin delivery data
- Experience contributing to FDA submissions or regulatory-facing clinical reports
- Familiarity with CDISC standards (SDTM/ADaM) or HEOR modeling
- Experience with EDC platforms, like Castor CDMS
- Interest in growing into manufacturing, quality, or commercial analytics as we approach approval
- MS in biostatistics, epidemiology, biomedical engineering, data science, or a related field
What Success Looks Like