Member of Technical Staff, Survival Analysis

About the role

This role advances clinical AI by creating models that forecast patient outcomes. The position directly supports precision medicine through survival analysis research and tool implementation. You join a team that delivers AI systems for oncology care at leading academic and community clinics.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Design survival analysis methods, and translate these methods into tools that predict patient outcomes.

Implement production-ready code from machine learning papers, and ensure methods integrate into clinical decision workflows.

Construct model evaluation frameworks, and use these frameworks to validate performance across clinical data modalities.

Co-author research papers and abstracts to share findings with the scientific and medical communities.

Co-mentor junior team members, and guide their contributions to survival analysis projects.

Requirements

Hold a PhD in machine learning or statistics, and apply this expertise to survival analysis problems.

Demonstrate excellent knowledge of survival analysis methods, and use this knowledge to evaluate model behavior.

Show passion for research, attention to detail, and the ability to drive tasks to completion in a clinical AI context.

Prefer candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics journals, especially those related to healthcare.

Exhibit excellent understanding of core machine learning concepts, and connect these concepts to survival analysis applications.

Show excellent knowledge of the foundations of statistics, linear algebra, probability, and machine learning for clinical data.

Apply strong Python skills and PyTorch proficiency to build and test survival analysis models.

Bring experience in deep learning, with bonus relevance for self-supervised learning, multi-modal learning, domain adaptation, causal inference, model interpretability, and computational pathology.

Practical notes

Work is conducted in a flat organizational structure where initiative shapes leadership roles.

Strong work ethic and ruthless prioritization are required to succeed in this role.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role focuses on research and methods that make disease predictable.

Core tools include survival analysis, foundation models, and causal inference techniques.

The team operates across academic and clinical settings worldwide.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.