Senior Data Scientist

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🕒 Maio 29

⛰️ Colorado, Nevada, +2 estados a mais – Remoto

⛰️ Colorado – Remote 🎰 Nevada – Remote 🗽 New York – Remote ☕ Washington – Remote

💵 $140.000 - $150.000 / ano

⏰ Tempo Integral

🟠 Sênior

📊 Cientista de Dados

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Pager Health

SiteLinkedInTodas as Vagas

201 - 500 funcionários

Fundada em 2014

⚕️ Seguro de Saúde

☁️ SaaS

🤖 Inteligência Artificial

Healthcare Insurance • SaaS • Artificial Intelligence

A Pager Health é uma empresa de plataforma de saúde conectada que permite que empresas de saúde ofereçam experiências de saúde inteligentes e de alto engajamento para seus pacientes, membros e equipes, por meio de tecnologia integrada, IA e serviços de concierge. Suas soluções proporcionam acesso seguro e suportam cuidados proativos e personalizados ao longo da jornada de saúde, melhorando o engajamento e a satisfação dos usuários.

Descrição

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions. • Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria. • Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems. • Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights. • Develop and maintain model documentation, technical specifications, and implementation plans. • Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence. • Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions. • Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact. • Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics. • Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk. • Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability. • Perform model validation, testing, and performance assessments prior to production deployment. • Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives. • Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes. • Define key performance indicators and success metrics for machine learning and AI initiatives. • Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods. • Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks. • Use experimentation and data-driven insights to guide product, operational, and strategic decisions. • Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments. • Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions. • Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs. • Support the creation of scalable, maintainable, and reliable AI and machine learning solutions. • Ensure model deployment processes align with engineering best practices and operational requirements. • Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions. • Communicate complex analytical findings and technical concepts to both technical and non-technical audiences. • Present recommendations, insights, and model performance results to leadership and project teams. • Support technical reviews, project planning, and delivery activities across cross-functional initiatives. • Contribute to knowledge sharing, documentation, and best practices within the data science organization. • Provide technical guidance and mentorship to junior team members and peers as needed.

🎯 Requisitos

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field; Master's degree preferred. • 7+ years of experience in data science, machine learning, advanced analytics, or a related field. • Demonstrated experience developing and deploying machine learning models in production environments. • Strong foundation in statistics, hypothesis testing, experimental design, and predictive modeling. • Experience working with large datasets and distributed data processing environments. • Proficiency in Python, SQL, and common data science and machine learning frameworks. • Experience communicating analytical findings and recommendations to business and technical stakeholders. • Proven ability to lead projects and collaborate effectively across cross-functional teams.

🏖️ Benefícios

• stock options • range of medical benefits • dental benefits • vision benefits • financial benefits • generous PTO • stipends for professional development • wellness benefits