Applied Scientist, AI

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.

Company Overview

A healthcare technology organization is reimagining how people access care by bringing healthcare services directly to patients’ homes. The organization uses technology inspired by marketplace and last-mile delivery platforms to improve access to preventive and chronic care, particularly for patients who face barriers to visiting traditional healthcare facilities.

What You Will Do

• Turn ambiguous healthcare, product, and operational problems into well-defined ML, AI, ranking, optimization, NLP, or LLM-based tasks.
• Build strong baselines and improve them efficiently using appropriate modeling approaches.
• Develop models using traditional machine learning, deep learning, NLP, and LLM-based techniques where appropriate.
• Design offline and online evaluations that are measurable, reliable, and predictive of real-world impact.
• Select metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes.
• Conduct detailed error analysis to improve model quality, product fit, and operational usefulness.
• Identify label leakage, selection bias, confounding, and other data artifacts before models reach production.
• Explore complex real-world datasets, evaluate label quality, and determine whether problems are ready for modeling.
• Partner with ML engineering teams to productionize models reliably and establish production-readiness requirements.
• Work with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge cases.
• Explain model tradeoffs, uncertainty, limitations, and expected impact to product, operations, clinical, and leadership teams.
• Prepare experiment documentation, summarize findings, and help teams determine when and how AI systems should be deployed.
• Pressure-test results to determine whether they are robust, meaningful, and useful before recommending production deployment.

What Gives You an Edge

• Master’s or PhD in computer science, statistics, machine learning, applied mathematics, operations research, biomedical informatics, epidemiology, or a related quantitative field.
• Exceptional applied experience that can substitute for formal graduate training.
• Expertise in areas such as LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI.
• Experience shipping models into production with measurable real-world impact.
• Experience working with healthcare data, including claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data.
• Experience working with PHI, HIPAA-aware systems, or other sensitive regulated data.
• Strong judgment regarding when traditional ML approaches are likely to outperform LLMs and when LLMs are appropriate.
• Experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts.
• Experience working in a startup or fast-moving applied environment where ambiguity, speed, and scientific rigor are important.

What Makes You Successful

• Understand how ML models work under the hood and explain technical concepts clearly to non-technical stakeholders.
• Maintain a strong focus on impact and recognize when a simpler model may be the most effective solution.
• Treat evaluation as a critical component of model development.
• Identify when metrics are misleading, incomplete, or disconnected from real-world outcomes.
• Detect leakage, bias, and confounding that may affect model performance.
• Move effectively between modeling, error analysis, stakeholder collaboration, and production handoff.
• Communicate model limitations clearly to both engineering teams and clinical stakeholders.
• Work comfortably with ambiguity and adapt modeling approaches to problems without established playbooks.
• Balance scientific rigor with the practical need to deliver useful systems.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

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