Senior Data Scientist - Commercial Product Strategy

📍 Job Overview

Job Title: Senior Data Scientist - Commercial Product Strategy

Company: CVS Health

Location: New York, NY, United States

Job Type: Full-Time

Category: Data Science & Analytics / Commercial Strategy

Date Posted: May 18, 2026

Experience Level: 5-10 years

Remote Status: Hybrid

🚀 Role Summary

• Drive commercial product strategy and measurable business outcomes by transforming complex healthcare data into actionable insights, with a focus on Aetna's Commercial Product organization.

• Develop and scale advanced analytics solutions, including AI-powered business advisory capabilities, to uncover trends and enable data-driven decision-making for over 15 million lives.

• Operate at the intersection of data science, product strategy, and commercial leadership, informing product design, pricing, go-to-market execution, and performance optimization.

• Translate complex technical analyses into clear, strategic recommendations for senior stakeholders across Product, Sales, Actuarial, Marketing, and Underwriting teams.

📝 Enhancement Note: This role is positioned as a Senior Data Scientist with a strong commercial and product strategy focus within the healthcare industry, specifically supporting Aetna's Commercial Product organization. The emphasis on AI-powered business advisory and driving measurable outcomes suggests a strategic, impact-oriented role rather than purely technical execution. The hybrid work arrangement implies a need for strong communication and collaboration skills to bridge remote and in-office interactions.

📈 Primary Responsibilities

• Develop, validate, and deploy advanced statistical models and machine learning algorithms to analyze large-scale healthcare datasets (claims, member behavior, etc.) for commercial product insights.

• Design and implement AI-powered business advisory tools and insight platforms to proactively identify growth opportunities, optimize product offerings, and inform pricing strategies.

• Partner closely with Product Management, Sales, Actuarial, and Marketing teams to translate data-driven insights into actionable product enhancements, pricing adjustments, and go-to-market strategies.

• Lead the development of experimentation frameworks and causal inference analyses to measure the impact of product changes, pricing strategies, and marketing campaigns on key commercial metrics.

• Create and maintain robust reporting dashboards and visualizations to communicate complex analytical findings and performance trends to senior leadership and cross-functional stakeholders.

• Contribute to the development of advanced forecasting models to predict market trends, member behavior, and financial performance for commercial products.

• Mentor and guide junior data scientists, fostering a culture of analytical rigor, innovation, and continuous learning within the team.

• Drive the application of agentic architecture and AI deployment strategies to enhance decision-making and automate insights generation within the commercial product lifecycle.

📝 Enhancement Note: The responsibilities highlight a blend of advanced technical data science skills with a strong commercial and strategic application. The emphasis on "AI-powered business advisory," "agentic architecture," and "revenue optimization" points to a need for practical application of AI/ML to directly influence business decisions and financial outcomes, not just theoretical modeling.

🎓 Skills & Qualifications

Education:

• Master’s degree (MS) or MBA in a quantitative or business-related field (e.g., Data Science, Statistics, Economics, Engineering, Operations Research, or similar) required.

Experience:

• 5+ years of experience in data science, advanced analytics, or analytics consulting.

• 7+ years of experience is preferred.

• Proven track record of translating complex data into actionable business insights and measurable outcomes.

• Experience working with large, complex datasets, particularly healthcare claims, customer behavioral data, or similarly structured enterprise data.

• Experience supporting commercial decision-making processes such as pricing, product strategy formulation, or go-to-market optimization.

Required Skills:

• Strong proficiency in statistical modeling, machine learning techniques, and data analysis using Python, R, SQL, or equivalent programming languages.

• Expertise in data manipulation, feature engineering, and model evaluation for complex datasets.

• Excellent communication skills, with a proven ability to distill complex analyses into clear, strategic recommendations for both technical and non-technical audiences.

• Strong problem-solving skills with a focus on identifying high-ROI opportunities and driving tangible business value.

Preferred Skills:

• Experience in healthcare analytics, specifically with payer/provider data, claims processing, or population health management initiatives.

• Experience developing and deploying AI/ML-driven products or decision-support tools, including familiarity with agentic architecture and deployment methodologies.

• Background in pricing analytics, actuarial partnership, or revenue optimization strategies within a commercial context.

• Experience working in product-centric organizations or directly supporting product lifecycle decisions from inception through ongoing optimization.

• Familiarity with experimentation frameworks, A/B testing, causal inference, and advanced forecasting techniques.

• Proven ability to lead or mentor junior data scientists and contribute to building high-performing analytics teams.

• Strong orientation toward value creation, financial impact, and client-centric outcomes within enterprise settings.

📝 Enhancement Note: The preferred qualifications strongly suggest that candidates with direct healthcare industry experience, particularly in payer analytics, will have a significant advantage. The mention of "agentic architecture" and "AI/ML-driven products" indicates a forward-looking role that embraces cutting-edge AI development and deployment, moving beyond traditional modeling. The emphasis on influencing senior stakeholders and driving ROI underscores the strategic business impact expected of this role.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable examples of translating complex data into actionable business insights that drove measurable outcomes, ideally within a commercial or product strategy context.

• Case studies showcasing the development and application of statistical models and machine learning algorithms to solve real-world business problems.

• Evidence of experience working with large, complex datasets, including data cleaning, feature engineering, and analysis methodologies.

• Documentation of projects where you influenced senior stakeholders or drove cross-functional alignment based on analytical findings.

• Examples of projects that supported commercial decision-making, such as pricing strategy, product design, or go-to-market execution.

Process Documentation:

• Showcase structured approaches to defining business problems and translating them into analytical frameworks.

• Include examples of your process for data exploration, model selection, validation, and deployment.

• Provide evidence of your methodology for communicating complex analytical insights to diverse audiences, including executive leadership.

• Detail your approach to collaborating with cross-functional teams (e.g., Product, Sales, Marketing) to implement data-driven recommendations.

• Illustrate your process for measuring the impact and ROI of analytical solutions.

📝 Enhancement Note: For this senior role, the portfolio should highlight not just technical skills but also strategic thinking and business impact. Candidates should be prepared to present case studies that clearly articulate the business problem, the analytical approach, the challenges encountered, the solutions developed, and most importantly, the quantifiable business outcomes and ROI achieved. The emphasis on influencing stakeholders and driving commercial decisions means portfolios should demonstrate leadership and strategic partnership.

💵 Compensation & Benefits

Salary Range:

The typical annual base salary range for this position in New York, NY is $111,240 - $284,280. This range represents the base compensation and does not include potential bonuses, commissions, or short-term incentive programs. The final offer will be determined by various factors including candidate experience, education, geographic location, and other relevant qualifications.

Benefits:

• Comprehensive Medical Insurance

• Comprehensive Dental Insurance

• Comprehensive Vision Insurance

• Paid Time Off (PTO)

• Retirement Savings Options (e.g., 401k)

• Wellness Programs

• Eligibility for CVS Health bonus, commission, or short-term incentive programs.

Working Hours:

• Standard full-time schedule of 40 hours per week.

• The hybrid work arrangement suggests flexibility in balancing remote and in-office work, requiring effective time management and communication to ensure productivity and collaboration.

📝 Enhancement Note: The provided salary range is broad, reflecting the senior level and the potential for significant experience variation. It's crucial for candidates to research comparable Senior Data Scientist roles in the New York City area, considering the specific industry (healthcare) and the emphasis on commercial strategy. The inclusion of bonus, commission, and short-term incentive programs indicates a performance-driven compensation structure, aligning with the role's focus on measurable business outcomes.

🎯 Team & Company Context

🏢 Company Culture

Industry: Healthcare and Pharmacy Services. CVS Health operates a vast integrated healthcare ecosystem, including retail pharmacies, pharmacy benefit management (PবিM), health insurance (Aetna), and retail clinics.

Company Size: CVS Health is a Fortune 50 company and a major employer globally. This large enterprise structure means operations are often complex and require navigating established processes while driving innovation.

Founded: CVS Health was founded in 1963. Its long history and growth through acquisitions (most notably Aetna) have created a diverse operational landscape with established best practices and ongoing integration efforts.

Team Structure:

• The Senior Data Scientist will likely be part of a dedicated analytics or data science team within the Commercial Product organization, potentially reporting into a Director or VP of Data Science or Product Analytics.

• This team will collaborate closely with Product Managers, Commercial Leaders, Sales Operations, Marketing teams, and Actuarial departments, requiring strong cross-functional partnership.

Methodology:

• Emphasizes a data-driven approach to decision-making, leveraging advanced analytics to understand member behavior, market trends, and product performance.

• Focuses on transforming complex healthcare data into actionable insights that directly impact business strategy, product development, pricing, and go-to-market execution.

• Values innovation in analytics, particularly in areas like AI/ML-powered business advisory and agentic architecture, to drive efficiency and competitive advantage.

• Prioritizes collaboration and communication to ensure analytical insights are understood and acted upon by business leaders.

Company Website: https://www.cvshealth.com/

📝 Enhancement Note: Understanding CVS Health's scale and its integrated model (retail, PBM, insurance) is crucial. For a Senior Data Scientist, this means access to incredibly rich and diverse datasets. The challenge and opportunity lie in navigating this complexity to extract meaningful insights that can influence strategic decisions across different business units, particularly within the Aetna commercial segment. The hybrid work arrangement suggests a company culture that is adapting to modern work styles while maintaining strong organizational structure.

📈 Career & Growth Analysis

Operations Career Level: This is a Senior Data Scientist role, indicating a mid-to-senior level position. It requires significant independent contribution, strategic thinking, and the ability to influence others. The role is focused on applying advanced analytical techniques to directly shape commercial product strategy and drive business outcomes, rather than solely on technical execution or research.

Reporting Structure: The role will likely report to a manager or director within the Commercial Product Analytics or Data Science division, with direct interaction with senior leaders in Product, Sales, Marketing, and Actuarial. This position offers visibility to executive-level decision-making.

Operations Impact: The Senior Data Scientist is expected to have a direct and measurable impact on CVS Health's commercial strategy by informing product design, optimizing pricing, enhancing go-to-market execution, and driving revenue growth for Aetna's commercial products. The focus on AI-powered business advisory highlights an ambition to create scalable, impactful solutions that lead to significant financial results and competitive advantages.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI/ML, agentic architectures, causal inference, and advanced forecasting.

• Leadership Development: Opportunities to mentor junior data scientists, lead analytical projects, and potentially transition into management roles.

• Strategic Influence: Gain exposure to executive-level strategy discussions and contribute to high-impact business decisions, potentially leading to roles in product management, strategy, or advanced analytics leadership.

• Industry Expertise: Develop specialized knowledge in healthcare analytics, commercial product strategy, and payer/provider dynamics within a leading healthcare organization.

• Cross-Functional Exposure: Work across diverse teams, gaining a holistic understanding of the healthcare business value chain.

📝 Enhancement Note: This role offers a clear path for growth within a large, established organization like CVS Health. The "Senior" title implies leadership potential, both technically and in terms of project ownership. Candidates looking to bridge advanced data science with tangible business strategy and impact in the healthcare sector will find significant opportunities here. The hybrid nature might also appeal to those seeking a balance between structured corporate environments and flexible working arrangements.

🌐 Work Environment

Office Type: The "TELECOMMUTE" location type combined with the "Hybrid" work arrangement suggests a modern office environment that supports flexible working. Employees are expected to work some days in the office and some remotely.

Office Location(s): New York, NY (161 Ave of the Americas). This location is in a major metropolitan hub, offering access to talent and resources. The company also operates across the United States.

Workspace Context:

• Collaboration: The hybrid model necessitates strong digital collaboration tools and in-person synergy. Expect a mix of virtual meetings, brainstorming sessions, and potential team offsites. The role requires active engagement with colleagues both in the office and remotely.

• Tools & Technology: Access to a robust technology stack, including high-performance computing, cloud environments, and specialized analytics software, will be available to support complex data science tasks.

• Team Interaction: Opportunities to interact with diverse teams, including product managers, sales leaders, marketing specialists, and actuaries, fostering a dynamic and cross-functional work environment. The emphasis on mentorship suggests a supportive team dynamic.

Work Schedule:

• A standard 40-hour work week is expected.

• The hybrid arrangement provides flexibility in managing work location, but core working hours will likely align with team availability for collaboration, particularly during critical project phases or for in-office days.

📝 Enhancement Note: The hybrid nature of this role is a key aspect of the work environment. Candidates should be comfortable with a mix of remote work and in-office presence, requiring self-discipline, strong communication skills, and the ability to collaborate effectively across different modalities. The New York City location offers access to a vibrant professional community and potential networking opportunities.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or Recruiter call to assess basic qualifications, interest, and cultural fit.

• Technical Assessment: May involve a coding challenge (e.g., SQL, Python scripting) or a take-home data science project to evaluate core technical skills.

• Hiring Manager Interview: In-depth discussion about experience, past projects, problem-solving approach, and alignment with role requirements. This is where your portfolio will be a key discussion point.

• Panel Interviews: Interviews with cross-functional stakeholders (e.g., Product Managers, Sales Leaders, Actuaries) to assess collaboration, communication, and business acumen.

• Senior Leadership Interview: Final discussion with a Director or VP to evaluate strategic thinking, leadership potential, and overall fit within the organization.

Portfolio Review Tips:

• Quantify Impact: For each project, clearly articulate the business problem, your role, the methodologies used, and most importantly, the quantifiable results (e.g., revenue increase, cost savings, efficiency gains, ROI). Use metrics relevant to commercial products and healthcare.

• Highlight Strategy: Showcase projects where your analytical work directly informed product strategy, pricing decisions, or go-to-market execution. Emphasize your ability to translate data into strategic recommendations.

• Showcase AI/ML Application: Include examples of developing and deploying AI/ML models, especially those that provided business advisory capabilities or drove automation. If applicable, mention experience with agentic architecture.

• Data Complexity: Demonstrate experience with large, complex datasets, particularly healthcare claims or member behavioral data. Explain your approach to data wrangling, feature engineering, and model validation.

• Communication Clarity: Be prepared to walk through your portfolio projects concisely, explaining technical concepts in a way that business stakeholders can understand. Focus on the "so what?" for each project.

Challenge Preparation:

• Case Study Practice: Prepare to discuss a complex business problem relevant to commercial product strategy in healthcare. Outline how you would approach it analytically, what data you would need, what models you might use, and how you would measure success.

• Stakeholder Scenarios: Practice articulating how you would influence senior leaders, gain buy-in for analytical initiatives, and collaborate effectively with non-technical teams.

• Technical Deep Dive: Be ready to discuss your favorite algorithms, model tuning strategies, and how you handle model interpretability and bias, especially in a regulated industry like healthcare.

📝 Enhancement Note: The interview process is designed to assess a candidate's full spectrum of skills, from technical proficiency to strategic influence. A well-curated portfolio that clearly demonstrates quantifiable business impact and strategic thinking is paramount. Candidates should prepare to discuss their experience with AI/ML applications and their ability to navigate complex healthcare data and regulations.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (highly preferred), R, SQL. Proficiency in libraries for data manipulation (e.g., Pandas, NumPy), machine learning (e.g., Scikit-learn, TensorFlow, PyTorch), and statistical analysis.

• Data Warehousing & Databases: Experience with large-scale data environments, potentially including cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and relational databases.

• Cloud Platforms: Familiarity with cloud computing environments like AWS, Azure, or GCP for data storage, processing, and model deployment.

Analytics & Reporting:

• BI & Visualization Tools: Tableau, Power BI, or similar tools for creating interactive dashboards and reports to communicate insights to stakeholders.

• Statistical Software: Familiarity with statistical packages and modeling environments.

• Experimentation Platforms: Experience with A/B testing frameworks and tools for measuring product and marketing effectiveness.

CRM & Automation:

• Healthcare Data Platforms: Experience with specific healthcare data platforms or EMR/EHR data if applicable.

• ETL/ELT Tools: Understanding of data ingestion and transformation processes.

• MLOps Tools: Familiarity with tools and practices for deploying, monitoring, and managing machine learning models in production environments.

📝 Enhancement Note: The technology stack will likely be robust, given CVS Health's scale. Expertise in Python for data science, SQL for data querying, and familiarity with cloud platforms and BI tools are essential. The mention of "AI-powered business advisory" and "agentic architecture" suggests that experience with MLOps and advanced deployment strategies for AI models will be highly valued. Candidates should be prepared to discuss how they leverage these tools to drive business outcomes.

👥 Team Culture & Values

Operations Values:

• Data-Driven Decision Making: A core value emphasizing the use of data and analytics to inform all strategic and operational decisions. Operations professionals are expected to champion data integrity and analytical rigor.

• Customer-Centricity: A commitment to understanding and serving the needs of members, patients, and customers. This translates to designing products and services that enhance the healthcare experience.

• Innovation and Agility: Encouraging new ideas and approaches, particularly in leveraging technology like AI/ML to improve processes and outcomes. The ability to adapt to a rapidly evolving healthcare landscape is key.

• Collaboration and Teamwork: Fostering a cooperative environment where diverse teams work together to achieve common goals, breaking down silos between departments.

• Accountability and Ownership: Taking responsibility for outcomes, driving initiatives to completion, and being accountable for the impact of analytical solutions.

Collaboration Style:

• Cross-Functional Integration: Strong emphasis on working closely with various departments, including Product, Sales, Marketing, IT, and Actuarial, to ensure alignment and drive integrated strategies.

• Data-Informed Partnerships: Collaborating with business leaders to understand their challenges and co-create analytical solutions that address their needs and drive value.

• Knowledge Sharing: Encouraging the sharing of insights, best practices, and learnings across teams to foster a collective growth environment.

• Feedback-Oriented: Openness to constructive feedback on analytical approaches and strategies, promoting continuous improvement.

📝 Enhancement Note: CVS Health's values emphasize a blend of analytical rigor, customer focus, and collaborative innovation, which is crucial for operations roles. For a Senior Data Scientist, this means not only being technically proficient but also being an effective communicator and collaborator, capable of translating complex data insights into actionable strategies that benefit members and the business.

⚡ Challenges & Growth Opportunities

Challenges:

• Data Complexity and Integration: Navigating and integrating vast, disparate datasets from various sources within a large enterprise like CVS Health can be challenging. Ensuring data quality and accessibility for advanced analytics is a continuous effort.

• Regulatory Environment: Operating within the highly regulated healthcare industry requires a strong understanding of compliance, data privacy (e.g., HIPAA), and ethical considerations in data science and AI development.

• Translating Insights into Action: Ensuring that complex analytical insights are effectively communicated and adopted by business stakeholders to drive meaningful change and achieve desired business outcomes can be a significant hurdle.

• Pace of Innovation: Keeping pace with rapid advancements in data science, AI/ML technologies, and the evolving healthcare landscape requires continuous learning and adaptation.

Learning & Development Opportunities:

• Advanced AI/ML Specialization: Opportunities to delve deeper into areas like agentic AI, reinforcement learning, and advanced NLP for healthcare applications.

• Healthcare Domain Expertise: Gaining in-depth knowledge of commercial health insurance products, payer-provider dynamics, and population health management strategies.

• Leadership and Mentorship: Developing leadership skills through project management, mentoring junior team members, and contributing to strategic initiatives.

• Cross-Functional Exposure: Broadening understanding of business operations by collaborating with different departments, from product development to sales and marketing.

• Industry Conferences and Training: Access to professional development opportunities, conferences, and certifications relevant to data science and healthcare analytics.

📝 Enhancement Note: The challenges presented are typical for senior roles in large, regulated industries. Candidates should demonstrate an awareness of these challenges and a proactive approach to overcoming them. The growth opportunities highlight the potential for significant career advancement, both technically and in terms of strategic influence within the healthcare sector.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used advanced analytics or machine learning to solve a complex commercial product strategy problem. What was the outcome, and what was your specific contribution?" (Focus on quantifiable impact, strategic thinking, and your role.)

• "How would you approach developing an AI-powered business advisory tool for our commercial product sales team? What data would you need, what models might you consider, and how would you measure its success?" (Demonstrate understanding of AI deployment, business value, and measurement.)

Company & Culture Questions:

• "What interests you about CVS Health and specifically this role within Aetna's Commercial Product organization?" (Research CVS Health's mission, recent news, and the healthcare industry's challenges.)

• "How do you see data science contributing to the future of healthcare product strategy, and how does that align with CVS Health's vision?" (Showcase your understanding of the industry and company's direction.)

Portfolio Presentation Strategy:

• Structure for Impact: Organize your portfolio around key achievements, focusing on 2-3 impactful projects. For each, clearly define the problem, your solution, the technologies used, and the quantifiable business results.

• Storytelling with Data: Narrate your projects, emphasizing the 'why' and the 'so what.' Explain the business context and how your analytical work drove strategic decisions or measurable improvements.

• Focus on ROI: Be prepared to discuss the return on investment (ROI) or business value generated by your projects. Use concrete numbers and metrics whenever possible.

• Technical Depth vs. Business Acumen: Balance technical details with clear explanations of business implications. Be ready to answer questions at both a technical and strategic level.

• Tailor to CVS Health: If possible, subtly tailor your examples or explanations to align with CVS Health's values and strategic priorities in healthcare and commercial products.

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of deep technical expertise and strategic business acumen. Candidates should be ready to articulate their impact with quantifiable results, showcase their understanding of AI/ML applications in a commercial context, and demonstrate their ability to influence senior stakeholders. Researching CVS Health's current strategic initiatives and challenges in the healthcare market will be crucial.

📌 Application Steps

To apply for this operations position:

• Submit your application through the CVS Health Careers portal via the provided URL.

• Tailor Your Resume: Highlight experience and skills directly relevant to data science, commercial product strategy, AI/ML, and healthcare analytics. Use keywords from the job description, such as "statistical modeling," "machine learning," "Python," "SQL," "product strategy," "pricing strategy," and "healthcare analytics." Quantify achievements wherever possible.

• Prepare Your Portfolio: Curate 2-3 of your most impactful projects that demonstrate your ability to drive business outcomes through data science. Focus on projects that involved commercial strategy, product development, or revenue optimization, ideally within a complex data environment like healthcare. Ensure you can clearly articulate the business problem, your methodology, and the quantifiable results.

• Research CVS Health and Aetna: Understand the company's mission, values, and recent strategic initiatives, particularly within the commercial products space. Familiarize yourself with Aetna's offerings and the broader healthcare industry landscape.

• Practice Interview Questions: Rehearse answers to common data science and behavioral interview questions, with a specific focus on strategy, problem-solving, and stakeholder management within a commercial context. Be prepared to discuss your portfolio in detail.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.