Senior Data Scientist

We're looking for a Senior Data Scientist to lead high-priority cross-functional data science and AI initiatives that drive measurable business impact across our products and operations. This role is responsible for developing evaluating deploying and monitoring AI solutions and machine learning while partnering closely with Product Engineering Analytics and business stakeholders.
The ideal candidate combines practical experience building production-ready AI systems with strong statistical and machine learning expertise. They will translate complex business problems into scalable analytical solutions establish rigorous evaluation frameworks and ensure models deliver reliable business outcomes.

Responsibilities:
Data Science & Machine Learning

• 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.

• 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.

• Experience developing and evaluating generative AI LLM RAG or AI agent solutions.
• Experience designing model evaluation frameworks and benchmarking methodologies.
• Familiarity with MLOps practices model monitoring and production AI systems.
• Experience with cloud platforms such as AWS Azure or Google Cloud.
• Experience in healthcare healthcare technology digital health or other regulated industries.
• Knowledge of responsible AI principles model explainability techniques and bias mitigation approaches.

• Delivery of high-impact data science and AI solutions that improve business outcomes.
• Development of accurate scalable and reliable machine learning models.
• Establishment of effective model evaluation validation and monitoring practices.
• Demonstrated impact through experimentation measurement and data-driven decision-making.
• Strong collaboration with Product Engineering Analytics and business stakeholders.
• Clear communication of insights recommendations and model performance to leadership and cross-functional teams.

For Colorado Nevada New York and Washington DC-based employment: In accordance with the Pay Transparency laws the pay range for this position is $140000 to $150000. The compensation package may include stock options plus a range of medical dental vision financial generous PTO stipends for professional development and wellness benefits.  Final compensation for this role will be determined by various factors such as a candidate's relevant work experience skills certifications and geographic location. The range listed only applies to Colorado Nevada New York and Washington DC.

What We Do

Pager’s virtual care platform enables healthcare organizations to help their members and patients navigate the entire healthcare journey with a personalized connected care experience. By bringing together clinical and service teams onto a unified collaborative communication platform Pager enables delivery of a complete set of virtual care services including triage telemedicine appointment scheduling prescriptions labs and care advocacy to help your members make better health and benefits decisions. Pager was founded in 2014 in New York City and its platform supports over 15 million people across the United States and Latin America. For more information visit pager.com.

Why Work With Us

Our teams are made up up passionate individuals who care about our mission and enjoy working together! At Pager you'll have the opportunity to shape and stretch your own career while making a real impact in today's healthcare landscape!

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