Senior Manager, Data Science & Advanced Analytics

LivaNova•Arvada, CO

2d•$170,000 - $190,000•Remote

About The Position

The Senior Manager leads the day-to-day delivery of high-impact data science and advanced analytics across business unit and enterprise stakeholders. The team enables data-driven decision-making and supports stakeholder automation and AI needs using dashboarding, statistical modeling, machine learning, forecasting, survival analysis, AI-enabled solutions, and self-service tools. This player-coach leader manages and develops applied data scientists and analysts, shapes a prioritized portfolio of work, and partners with stakeholders to translate business questions into scalable analytical products and actionable recommendations. The role combines technical judgment and hands-on contributions with people leadership, stakeholder alignment, quality standards, and adoption. The position is US Remote, with optional hybrid work in Houston, Arvada, or NYC. Limited travel, generally less than 10%, may be required. The estimated annual base salary is $170,000–$190,000, plus a discretionary annual bonus; pay ranges may vary by location.

Requirements

Bachelor’s degree in data science, statistics, mathematics, computer science, engineering, economics, or a related quantitative field.
7+ years of experience applying data science and advanced analytics to real-world business problems, including ownership from problem definition through adoption and measurement.
3+ years of people leadership, including coaching technical team members, reviewing work, setting standards, and coordinating delivery across multiple projects.
Expertise in Python or R and strong working knowledge of SQL.
Experience with modern cloud analytics environments and version-controlled development workflows.
Knowledge of statistical modeling and machine learning methods, with sound judgment about when simpler analytical approaches are more appropriate.
Experience building trusted partnerships with commercial, marketing, sales, operations, finance, strategy, or other business functions.
Ability to synthesize complex analyses into concise, decision-oriented recommendations for senior leaders and non-technical audiences.
Experience developing self-service analytics, repeatable products, or transition plans that shift routine work closer to business users.
Excellent prioritization, organizational, and change-management skills in a fast-paced, evolving environment.
Working knowledge of responsible AI, data privacy, model governance, and validation practices.
Limited travel, generally less than 10%, may be required.

Nice To Haves

An advanced degree is strongly preferred.
Experience in a regulated industry.
Healthcare, medical-device, CRM, electronic health record, or claims-data experience.
Interest in learning about other areas of the business, such as commercial, operations, finance, or clinical.

Responsibilities

Lead, coach, and develop data scientists and analysts, setting expectations for technical quality, business impact, documentation, communication, and professional growth.
Manage the day-to-day portfolio across commercial and enterprise stakeholders, balancing strategic initiatives, recurring commitments, and urgent needs.
Partner with business leaders to identify opportunities for advanced analytics, machine learning, forecasting, causal inference, segmentation, optimization and automation, and AI-enabled decision support.
Translate ambiguous business questions into well-scoped analytical products with defined users, success measures, timelines, risks, and adoption plans.
Provide final review and technical guidance on analytical design, model selection, validation, interpretation, explainability, bias assessment, and communication of limitations.
Transition basic reporting, routine data pulls, and repeatable analyses to governed self-service solutions or appropriate business owners, including enablement and handoff plans.
Build reusable approaches, shared standards, and scalable workflows to reduce duplication and improve speed, consistency, and maintainability.
Communicate insights as clear business stories with recommendations, trade-offs, and next steps tailored to technical and executive audiences.
Work with IT, data, privacy, legal, compliance, and business teams on data access, platform needs, deployment, governance, and responsible use of analytics and AI.
Oversee measurement of solution adoption, model performance, operational outcomes, and business value, using results to guide iteration and prioritization.
Support talent planning, hiring, resource allocation, vendor or contractor coordination, and development of the team operating model.
Maintain sufficient hands-on technical depth to guide complex work, troubleshoot critical issues, and contribute directly to priority analyses when appropriate.