<p><strong>Opportunity Overview:</strong></p> <p>We are seeking a Technical, Hands-on Manager to lead a team <strong>responsible for building and maintaining high-quality healthcare market datasets and analytics that power internal insights, benchmarking, and external thought leadership</strong>.</p> <p>In this role, <strong>you will lead a team of data analysts</strong> responsible for the development, quality assurance, and ongoing refresh of market data assets. <strong>You will combine strong people leadership with technical expertise in analytics and data science</strong> to ensure reliable, scalable data pipelines and actionable insights.</p> <p>The ideal candidate is both a strong people manager and a hands-on analytics <strong>leader who can guide analysts in rigorous data methodology, translate data outputs into meaningful business insights, and partner closely with commercial strategy, product, clinical, and analytics stakeholders.</strong></p> <p><strong>What you’ll do:</strong></p> <ul> <li>Lead and develop a team of data analysts responsible for the creation, validation, and ongoing refresh of healthcare market datasets.</li> <li>Mentor analysts in data methodology, statistical reasoning, and reproducible analytics practices to ensure consistent and rigorous analysis across the team.</li> <li>Establish analytic standards, coding practices, and documentation expectations to ensure all datasets and insights produced by the team are reproducible and scalable.</li> <li>Establish and maintain governance processes for market data including versioning, documentation, auditability, and traceability of data sources and methodologies.</li> <li>Oversee change-detection logic, ensuring the team systematically identifies and documents additions, removals, and shifts in the market dataset.</li> <li>Define and enforce data quality standards across ingestion, transformation, and analysis workflows.</li> <li>Collaborate with Data Engineering and Platform teams to ensure the underlying data infrastructure supports scalable ingestion, transformation, and analytics workflows used by the analyst team.</li> <li>Translate analytic outputs into business insights, benchmarks, and structured datasets that support product strategy, commercial enablement, and thought leadership initiatives.</li> <li>Drive the implementation of project scope definition, effort estimation, and planning in close coordination with cross-functional teams.</li> <li>Conduct code reviews and analytics methodology reviews to ensure maintainable, reproducible data workflows and high-quality analytic outputs.</li> </ul> <p><strong>ISMS roles and responsibilities:</strong></p> <ul> <li>Good knowledge of Information practices.</li> <li>Assist the manager in all the information security activities implementation and maintenance process.</li> <li>Ensuring the team and imparted with Competence related to Information security</li> <li>Responsible for implementation of security policies and procedures and report any issues to the Information Security Manager.</li> </ul> <p><strong>Required Qualifications:</strong></p> <p><strong>Must-haves</strong></p> <ul> <li>7+ years of professional experience in data analytics, data science, or quantitative analysis in a production environment.</li> <li>Strong proficiency in Python and data analysis libraries (Pandas, NumPy, SciPy, Matplotlib).</li> <li>Advanced SQL querying and ability to work with large datasets.</li> <li>Strong understanding of statistics, data analysis methods, and pattern detection in complex datasets.</li> <li>Experience managing multiple concurrent analytics projects and prioritizing work across a team of analysts.</li> <li>Experience designing analytic frameworks or benchmark datasets used by business teams or external stakeholders.</li> <li>Experience managing data quality, data governance, and dataset lifecycle management.</li> <li>Experience evaluating and applying modern analytics or AI techniques (including LLM-based tools where appropriate) to enrich datasets or accelerate analytic workflows.</li> <li>Experiment and rapidly prototype design frameworks and validate hypotheses efficiently.</li> <li>Strong software engineering fundamentals including modular Python code, reusable analytic functions, and maintainable data pipelines.</li> <li>Familiarity with cloud data platforms (e.g., AWS, GCP, or Azure) and common data services used to store, process, and query large datasets.</li> <li>Work seamlessly across Data Science, Engineering, Product, DevOps, and Data Engineering teams to align technical decisions with business goals, ensuring smooth integration of AI/ML capabilities into production systems</li> <li>Excellent communication and storytelling skills, capable of translating complex AI/ML concepts into clear, actionable insights for leadership, stakeholders, and non-technical partners.</li> <li>Ownership mindset with end‑to‑end accountability, ensuring delivery from concept to deployment to monitoring—while continuously identifying risks, blockers, and opportunities for improvement.</li> </ul> <p><strong>Nice-to-haves</strong></p> <ul> <li>Background in healthcare payment integrity, clinical data workflows, or payer/provider analytics.</li> <li>Thought leadership in analytics-driven market intelligence, benchmarking, or healthcare data insights.</l