Mid Level Decision Scientist Jobs
Mid level decision scientist jobs go to analysts ready to own end-to-end analytical projects, translate ambiguous business questions into rigorous frameworks, and guide decisions with limited oversight. Openings are concentrated in Insurance, Science & Research, and Technology & Software, with 50% remote or hybrid availability, and employers like Fluidstack, City of Hope, and Highmark Health hiring at this level now.
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Become part of the Beebe team - an inclusive team positioned in a vibrant, coastal community. Enjoy a fulfilling career as you support the health of our patients and a team focused on excellence.
Overview:The Data Network Manager serves as a senior analytics leader and subject matter expert responsible for driving enterprise level analytics strategy, leading complex cross functional initiatives, and shaping data governance across the organization. This role blends advanced analytical expertise, project leadership, and organizational governance, including oversight of multiple committees that influence data standards, performance measurement, and strategic planning.
A core component of this role is leveraging Epic data assets including Clarity, Caboodle, and Epic reporting tools to deliver high impact insights that support clinical, operational, and strategic decision making.
Responsibilities: Enterprise Analytics Leadership
Serve as a senior expert and thought leader in data analytics, Epic data structures, and enterprise reporting.
Guide the development of advanced analytics solutions, dashboards, and models using Epic and nonepic data sources.
Establish best practices for data quality, governance, and analytical methodologies.
Partner with IT, Epic analysts, and data engineering teams to optimize data pipelines and reporting architecture.
Strategic Project Leadership
Lead high visibility, cross functional analytics projects that support organizational priorities.
Develop project frameworks, timelines, and resource plans; monitor progress and mitigate risks.
Coordinate collaboration across clinical, operational, financial, and IT teams.
Present findings, recommendations, and strategic insights to senior leadership and executive stakeholders.
Epic Data Integration & Optimization
Oversee the use of Epic tools such as Reporting Workbench, Slicer Dicer, Clarity extracts, and Caboodle.
Provide guidance on Epic data model interpretation, data availability, and reporting feasibility.
Collaborate with Epic application teams to align workflows, documentation, and reporting needs.
Support the development of Epic based self-service analytics capabilities.
Committee Oversight & Governance
Provide leadership, facilitation, or subject matter expertise for key committees, such as:
Data Governance Committee
Epic Reporting or Data Stewardship Committees
Analytics Steering Committee
Quality, Performance Measurement, or Strategic Planning Committees
Shape committee agendas, guide decision-making, and ensure alignment with organizational strategy.
Maintain documentation, transparency, and follow-through on committee actions.
Stakeholder Engagement & Communication
Act as a trusted advisor to leaders across the organization, helping them interpret and apply Epic derived insights.
Translate complex analytical concepts into clear, actionable recommendations.
Promote data literacy and support adoption of analytics tools and governance practices.
Qualifications:Required
- Bachelor's degree in Data Analytics, Statistics, Computer Science, Health Informatics, Business, or related field.
- 5+ years of experience in analytics, business intelligence, or project management roles.
- Demonstrated success leading complex, cross functional analytics projects.
- Strong communication, facilitation, and stakeholder management skills.
Preferred
- Epic certifications or proficiencies (e.g., Clarity Data Model, Cogito, Caboodle).
- Experience in healthcare quality, clinical operations, population health, or revenue cycle analytics.
- Experience leading or supporting governance bodies or enterprise committees.
- Familiarity with enterprise data governance frameworks.
Competencies Skills:
Key Competencies
- Strategic thinking
- Advanced analytics expertise
- Epic data literacy
- Project and program leadership
- Data storytelling
- Governance and facilitation
- Cross functional collaboration
- Change management
Education:
- Bachelor's degree in Data Analytics, Statistics, Computer Science, Health Informatics, Business, or related field.
Become part of the Beebe team - an inclusive team positioned in a vibrant, coastal community. Enjoy a fulfilling career as you support the health of our patients and a team focused on excellence.
Overview:The Data Network Manager serves as a senior analytics leader and subject matter expert responsible for driving enterprise level analytics strategy, leading complex cross functional initiatives, and shaping data governance across the organization. This role blends advanced analytical expertise, project leadership, and organizational governance, including oversight of multiple committees that influence data standards, performance measurement, and strategic planning.
A core component of this role is leveraging Epic data assets including Clarity, Caboodle, and Epic reporting tools to deliver high impact insights that support clinical, operational, and strategic decision making.
Responsibilities: Enterprise Analytics Leadership
Serve as a senior expert and thought leader in data analytics, Epic data structures, and enterprise reporting.
Guide the development of advanced analytics solutions, dashboards, and models using Epic and nonepic data sources.
Establish best practices for data quality, governance, and analytical methodologies.
Partner with IT, Epic analysts, and data engineering teams to optimize data pipelines and reporting architecture.
Strategic Project Leadership
Lead high visibility, cross functional analytics projects that support organizational priorities.
Develop project frameworks, timelines, and resource plans; monitor progress and mitigate risks.
Coordinate collaboration across clinical, operational, financial, and IT teams.
Present findings, recommendations, and strategic insights to senior leadership and executive stakeholders.
Epic Data Integration & Optimization
Oversee the use of Epic tools such as Reporting Workbench, Slicer Dicer, Clarity extracts, and Caboodle.
Provide guidance on Epic data model interpretation, data availability, and reporting feasibility.
Collaborate with Epic application teams to align workflows, documentation, and reporting needs.
Support the development of Epic based self-service analytics capabilities.
Committee Oversight & Governance
Provide leadership, facilitation, or subject matter expertise for key committees, such as:
Data Governance Committee
Epic Reporting or Data Stewardship Committees
Analytics Steering Committee
Quality, Performance Measurement, or Strategic Planning Committees
Shape committee agendas, guide decision-making, and ensure alignment with organizational strategy.
Maintain documentation, transparency, and follow-through on committee actions.
Stakeholder Engagement & Communication
Act as a trusted advisor to leaders across the organization, helping them interpret and apply Epic derived insights.
Translate complex analytical concepts into clear, actionable recommendations.
Promote data literacy and support adoption of analytics tools and governance practices.
Qualifications:Required
- Bachelor's degree in Data Analytics, Statistics, Computer Science, Health Informatics, Business, or related field.
- 5+ years of experience in analytics, business intelligence, or project management roles.
- Demonstrated success leading complex, cross functional analytics projects.
- Strong communication, facilitation, and stakeholder management skills.
Preferred
- Epic certifications or proficiencies (e.g., Clarity Data Model, Cogito, Caboodle).
- Experience in healthcare quality, clinical operations, population health, or revenue cycle analytics.
- Experience leading or supporting governance bodies or enterprise committees.
- Familiarity with enterprise data governance frameworks.
Competencies Skills:
Key Competencies
- Strategic thinking
- Advanced analytics expertise
- Epic data literacy
- Project and program leadership
- Data storytelling
- Governance and facilitation
- Cross functional collaboration
- Change management
Education:
- Bachelor's degree in Data Analytics, Statistics, Computer Science, Health Informatics, Business, or related field.
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Top Industries Hiring
- Insurance
- Science & Research
- Technology & Software
- Healthcare & Medical Services
- Education
Mid Level Decision Scientist Jobs: Frequently Asked Questions
How do I get a mid level decision scientist job?
Position yourself around ownership, not just contribution. Highlight projects where you defined the analytical approach, delivered recommendations to stakeholders, and saw measurable outcomes. Mid level roles want candidates who can operate independently, so your resume and interviews should emphasize the decisions your work influenced, the methods you chose, and how you communicated findings to non-technical audiences.
Which companies hire mid level decision scientists?
Companies hiring mid level decision scientists right now include Fluidstack, City of Hope, and Highmark Health, based on current listings on Migrate Mate as of November 2026. At this level, hiring tends to come from data-mature organizations in tech, finance, retail, and healthcare that have established analytics functions and need scientists who can run projects independently rather than under constant direction.
Are there remote mid level decision scientist jobs?
Yes, remote and hybrid options are common at this level. About 50% of mid level decision scientist openings are remote or hybrid as of November 2026, reflecting how analytical work in this role translates well across distributed teams. On-site roles do exist, particularly at companies where cross-functional collaboration with product or engineering teams is core to the work.
How do I move up to a mid level decision scientist role?
Moving from early-career into a mid level decision scientist role takes deliberate progression beyond executing assigned tasks. Focus on taking ownership of a full analytical problem, from framing the question to delivering the recommendation. Build fluency in experimentation, causal inference, or optimization depending on your domain. Demonstrating that your work changed a decision or outcome, not just produced an output, is what separates mid level candidates from junior ones.
Which industries hire the most mid level decision scientists?
Mid Level decision scientist roles concentrate in Insurance, Science & Research, and Technology & Software, based on current listings on Migrate Mate as of November 2026. These sectors invest heavily in data-informed decision-making at scale, and mid level scientists are well positioned there because they can own analytical workstreams independently without requiring the same oversight as earlier-career hires.