Manager - Advanced Analytics & Digital Science

Job Summary

The Manager - Advanced Analytics Data Analytics leads a multidisciplinary team responsible for delivering clear insights, advanced analytics, predictive solutions, and executive-ready recommendations in support of HR transformation and workforce business priorities.

This role provides people leadership, work prioritization, analytical oversight, and stakeholder partnership across a portfolio of reporting, insight generation, statistical analysis, machine learning, experimentation, and decision-support work.

The Manager is accountable for ensuring the team produces high-quality, well-governed, actionable work that helps leaders understand what is happening, why it matters, and what actions should be considered.

This role partners with business stakeholders, technology teams, product owners, and governance partners to identify priority needs, translate them into analytical work, and ensure delivery of solutions that are accurate, scalable, and aligned to business goals

Responsibilities

Team Leadership Development

• Lead, coach, and develop a team of analysts and data scientists across multiple experience levels
• Set expectations, priorities, and development goals aligned to business and organizational objectives.
• Provide regular feedback, mentoring, and career guidance to strengthen team capability.
• Support hiring, onboarding, performance management, and succession planning for the function.
• Create a culture of accountability, collaboration, analytical rigor, and continuous improvement.
• Balance team capacity and assign work to match skills, priorities, and delivery timelines.

Strategic Partnership Prioritization

• Partner with HR leaders, business stakeholders, product owners, technology teams, and governance partners to identify priority needs and translate them into analytical work.
• Help define the right problem, clarify decision points, and align stakeholders on scope, success measures, and expected outcomes.
• Serve as a trusted advisor by framing trade-offs, interpreting findings, and recommending practical next steps.
• Influence prioritization across the portfolio to ensure the team is focused on the highest-value opportunities.

Analytical Oversight Delivery

• Oversee the delivery of insight narratives, dashboards, recurring performance reviews, scorecards, statistical analyses, predictive models, and other analytical products.
• Ensure team outputs are accurate, documented, reproducible, and appropriate for the intended audience and use case.
• Review complex work for methodological soundness, business relevance, clarity, and readiness for stakeholder use.
• Guide the team in selecting appropriate analytical methods, data sources, tools, and storytelling approaches.
• Support the translation of findings into decisions, recommendations, and measurable actions.

Data Science Business Insights Leadership

• Provide oversight across both business insights and data science workstreams, ensuring the team can move from descriptive reporting to advanced analytics and modeling when appropriate.
• Encourage strong partnership between analysts, data scientists, business stakeholders, data engineers, and governance partners.
• Support the development and operationalization of analytical solutions, including model monitoring, feedback loops, and adoption support.
• Promote reusable frameworks, templates, playbooks, and standards that improve consistency and maturity across the function.

Governance, Quality Responsible AI

• Establish and reinforce standards for documentation, peer review, data quality, analytical rigor, and model governance.
• Ensure appropriate use of data, with attention to privacy, security, compliance, and responsible handling of sensitive information.
• Promote sound AI practices, including validation of AI-assisted outputs, human review, and responsible use of approved tools.
• Ensure analytical and AI-enabled work is explainable, transparent, bias-aware, and appropriate for business decision-making.
• Identify and mitigate risks related to data limitations, model assumptions, stakeholder interpretation, and operational readiness.

Communication Executive Presence

• Prepare and deliver clear, concise, business-relevant updates for senior stakeholders and leadership.
• Translate technical findings into practical business language and actionable recommendations.
• Communicate progress, risks, dependencies, and issue resolution clearly and proactively.
• Build confidence with stakeholders through credibility, responsiveness, and sound judgment.

AI Preparedness Expectations:

• Uses approved AI tools to accelerate planning, synthesis, documentation, quality review, analysis support, and stakeholder materials while validating outputs before use.
• Identifies appropriate AI augmentation opportunities within assigned workstreams and helps the team adopt safe, practical, and repeatable AI-enabled practices.
• Reviews AI-assisted outputs for accuracy, bias, completeness, privacy risk, governance alignment, and business context before release or recommendation.
• Builds deeper capability in AI-enabled analytics, workflow automation, decision support, and human-in-the-loop review

Education Experience:

• Bachelor's degree in Business, Computer Science, Machine Learning, Data Analytics, Statistics, Engineering, Economics, or a related field; equivalent experience may be considered.
• Typically 6+ years of experience in analytics, business intelligence, data science, consulting, healthcare analytics, operations analytics, or a related field.
• Experience leading people, projects, or cross-functional analytics initiatives required.
• Experience mentoring analysts, data scientists, or technical professionals strongly preferred.
• Experience presenting recommendations and analytical findings to senior leaders preferred.
• Healthcare, workforce, operational, transformation, or enterprise analytics experience preferred.

Must Have Skills:

• Strong people leadership, coaching, and talent development skills.
• Ability to manage a blended team of analysts and data scientists across multiple levels.
• Strong business acumen and the ability to connect analytics to operational and strategic priorities.
• Excellent communication and stakeholder management skills.
• Ability to review and guide work across reporting, statistical analysis, forecasting, machine learning, and insight generation.
• Strong understanding of data quality, analytical methods, model evaluation, and responsible AI principles.
• Proficiency with SQL, Python, R, and analytics or visualization tools such as Tableau, Power BI, or similar platforms.
• Ability to manage competing priorities in a fast-paced, matrixed environment.
• Commitment to data integrity, confidentiality, compliance, and practical business impact.

Nice to Have Skills:

• Experience leading both business insights and data science functions

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.