Senior Data Scientist

Job not on LinkedIn

🕒 5 days ago

⛰️ Colorado, Nevada, +2 more states – Remote

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

💵 $140k - $150k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

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Pager Health

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2014

⚕️ Healthcare Insurance

☁️ SaaS

🤖 Artificial Intelligence

Healthcare Insurance • SaaS • Artificial Intelligence

Pager Health is a connected health platform company that enables healthcare enterprises to deliver high-engagement, intelligent health experiences for their patients, members, and teams through integrated technology, AI, and concierge services. Their solutions provide secure access and support proactive, personalized care across the healthcare journey, improving engagement and satisfaction for users.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

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