Staff Data Scientist, Clinical Performance

🕒 Abril 24

🏄 California, New York – Remoto

🏄 California – Remote 🗽 New York – Remote

💵 $160.000 - $200.000 / ano

⏰ Tempo Integral

🔴 Especialista

📊 Cientista de Dados

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Pearl Health

SiteLinkedInTodas as Vagas

51 - 200 funcionários

⚕️ Seguro de Saúde

☁️ SaaS

💸 Finanças

💰 $18.000.000 Series A em 2021-09

Healthcare Insurance • SaaS • Finance

A Pearl Health é uma empresa que capacita e habilita os provedores de cuidados primários e equipes de coordenação de cuidados, oferecendo soluções tecnológicas para o atendimento baseado em valor. Eles se concentram em democratizar o acesso ao valor em saúde por meio de ferramentas que estabilizam o fluxo de caixa, recompensam o cuidado de qualidade ao paciente e simplificam os insights de dados. Com a Pearl Platform, os provedores têm acesso a insights de pacientes baseados em dados e sugestões acionáveis para manter a saúde dos pacientes de forma mais eficaz. A Pearl Health colabora com profissionais de saúde para melhorar os resultados de saúde, reduzir custos e aprimorar a experiência do paciente por meio de insights avançados de dados e plataformas tecnológicas que apoiam modelos de cuidados baseados em valor, como o ACO REACH.

Descrição

• Lead the design and implementation of advanced causal inference and statistical frameworks to measure and forecast the effectiveness of Pearl’s clinical products and operational services. • Design and build the scalable systems required to conduct rigorous impact analyses, moving beyond simple correlations to isolate the true "Pearl Effect" on patient populations. • Develop predictive models to issue forecasts for clinical quality measures (including eCQMs in MSSP and claims-based measures in REACH and LEAD). • Partner with other Staff Data Scientists to refine and validate patient risk models, ensuring that "rising acuity" signals are integrated effectively into our performance evaluation loops. • Partner with Engineering and Analytics to build robust data pipelines and ML infrastructure that support automated, repeatable performance measurement. • Collaborate with Product and Clinical Operations leaders to turn complex statistical findings into actionable narratives that influence product roadmaps and practice coaching. • Architect and oversee AI-driven agents that autonomously manage the end-to-end lifecycle of our statistical models.

🎯 Requisitos

• A graduate degree (Masters or PhD) in a quantitative field such as Statistics, Economics, Biostatistics, or Epidemiology • 8+ years of experience in results-driven quantitative analysis. • Proven experience implementing causal inference methodologies (e.g., diff-in-diff, synthetic control, propensity score matching) in real-world, messy data environments. • Experience building time-series forecasts or risk-adjustment models, with a strong understanding of how to define and measure a baseline vs. an intervention effect. • Expert-level proficiency in Python and SQL, with the ability to write production-quality code and design scalable data architectures. • Experience building or significantly contributing to scalable data science systems and infrastructure within a modern cloud environment (AWS, Snowflake, dbt). Deep recent experience with AWS Sagemaker is a plus. • The ability to explain the nuances of a p-value, a risk score, or an identification strategy to a non-technical audience.

🏖️ Benefícios

• We offer a competitive benefits package. More on our careers page.