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Vp Data Science Jobs (NOW HIRING)

CRH

Vice President, Data & Analytics

Atlanta, GA · On-site

Vice President, Data & Analytics Architectural Products Group Atlanta, Georgia, United States Job ... data science. * Foster an agile, outcome-focused culture that can rapidly adapt to changing ...

OptimizeRx

Vice President, Data and Analytics Products

$200K - $220K/yr

Vice President, Data amp; Analytics Products About OptimizeRx OptimizeRx is a leading healthcare ... This leader will partner across Product, Engineering, Data Science, Commercial, Customer Success ...

AXS

VP, Data Engineering

San Francisco, CA · On-site

$212K - $273K/yr

The Role The Vice President of Data Engineering will lead AXS' global data engineering organization ... Collaborate with Data Science, Product, Ticketing Strategy, Marketing, Finance, AEG, and other ...

Meredith Corporation

NY · On-site

$315K - $375K/yr

Senior Vice President, Data Operations Location: New York, NY (Hybrid 3x a week in office, remote ... Partner with Product, Engineering, and Data Science leaders to align roadmaps with business ...

Hillenbrand

Vice President, Data & Analytics

Columbus, OH · On-site +1

$173K - $224K/yr

The Vice President, Data & Analytics provides executive leadership for the organization's data ... Bachelor's degree in Data Science, Information Systems, Computer Science, or related field - or ...

AXS

VP, Data Engineering

Los Angeles, CA · On-site

$194K - $250K/yr

The Role The Vice President of Data Engineering will lead AXS' global data engineering organization ... Collaborate with Data Science, Product, Ticketing Strategy, Marketing, Finance, AEG, and other ...

AXS

VP, Data Engineering

Los Angeles, CA · On-site

$194K - $250K/yr

The Role The Vice President of Data Engineering will lead AXS' global data engineering organization ... Collaborate with Data Science, Product, Ticketing Strategy, Marketing, Finance, AEG, and other ...

Flexential Corp.

VP, Data Center Operations

The Vice President, Data Center Operations will play a central role in ensuring that facilities perform safely and consistently, that teams are equipped and aligned, and that the operating model can ...

AXS

VP, Data Engineering

San Francisco, CA · On-site

$212K - $273K/yr

The Role The Vice President of Data Engineering will lead AXS' global data engineering organization ... Collaborate with Data Science, Product, Ticketing Strategy, Marketing, Finance, AEG, and other ...

Citigroup Inc.

VP Marketing Data Science

Manhattan, NY · On-site

Overview The VP, Marketing Data Science is a highly experienced and senior role, part of the larger Media Sciences team in the Client Org, reporting to the SVP Marketing Data Science. This role will ...

Flexential Corp.

VP, Data Center Operations

OR · On-site +1

The Vice President, Data Center Operations will play a central role in ensuring that facilities perform safely and consistently, that teams are equipped and aligned, and that the operating model can ...

Kaizen Analytix LLC

VP of Data Science

Atlanta, GA · On-site

Original Post Date: 4/3/2026 VP of Data Science Kaizen Analytix LLC, an analytics products and services company that gives clients unmatched speed to value through analytics solutions and actionable ...

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Vp Data Science information

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$41.5K

$142.5K

$201K

How much do vp data science jobs pay per year?

As of Sep 10, 2026, the average yearly pay for vp data science in the United States is $142,460.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $166,500.00 per year, depending on experience, location, and employer.

What does a VP of Data Science do?

A VP of Data Science leads and manages the data science team within an organization, setting the strategic vision for how data is used to drive business decisions. They oversee the development and implementation of data-driven solutions, ensure data quality and integrity, and collaborate with other executives to align data initiatives with company goals. Additionally, they mentor data scientists, manage budgets, and stay updated on the latest trends and tools in data science to keep their teams competitive.

What are the key skills and qualifications needed to thrive as a VP of Data Science?

To thrive as a VP of Data Science, you need advanced expertise in statistical analysis, machine learning, and big data, usually supported by a graduate degree in a quantitative field and extensive industry experience. Familiarity with tools like Python, R, SQL, cloud platforms (e.g., AWS, Azure), and data visualization systems, as well as experience managing enterprise data architectures, is crucial. Exceptional leadership, strategic thinking, and communication skills set top candidates apart in this role. These abilities are essential for guiding teams, influencing business decisions, and driving impactful data-driven strategies across the organization.

How does a VP of Data Science typically collaborate with cross-functional teams to drive business outcomes?

A VP of Data Science frequently works with product managers, engineering teams, and business stakeholders to align data initiatives with organizational goals. They play a strategic role in translating business challenges into data-driven solutions, ensuring that data science projects support decision-making and growth. Effective collaboration involves regular meetings, clear communication of technical concepts to non-technical audiences, and fostering a culture of data literacy across the organization. By bridging technical expertise and business acumen, the VP helps maximize the impact of data science initiatives.

What is the difference between Vp Data Science vs Data Science Manager?

AspectVp Data ScienceData Science Manager
ResponsibilitiesStrategic leadership, setting data science vision, overseeing multiple teamsManaging data science projects, team supervision, project delivery
Required CredentialsAdvanced degree (Master's/PhD), extensive experience, leadership skillsDegree in related field, experience in managing data projects
Work EnvironmentExecutive-level, cross-departmental collaboration, strategic planningTeam management, project-focused, collaborative with data teams

The Vp Data Science typically holds a strategic, leadership role overseeing multiple teams and setting long-term data initiatives, while a Data Science Manager focuses on managing data projects and teams directly involved in execution. Both roles require strong technical backgrounds, but the Vp is more involved in high-level planning and organizational strategy.

More about Vp Data Science jobs

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Cities with the most Vp Data Science job openings:

What are the most commonly searched types of Data Science jobs?

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Vp Data Science jobs near you
Infographic showing various Vp Data Science job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.

Vice President, Data & Analytics

Conshohocken, PA • On-site

Judge Group, Inc.
Recruiting and Staffing Services • 5 - 10K employees
Learn more about Judge Group, Inc.external

$200K - $250K/yr

Other

Posted 17 days ago

Job description

Location: West Conshohocken, PA Salary: $200,000.00 USD Annually - $250,000.00 USD Annually Description:
Vice President, Data & Analytics
Position Overview
Reporting directly to the Head of Technology, the Vice President of Data & Analytics will establish and lead the organization's enterprise data strategy and platform. This executive-level leader will oversee the complete data lifecycle, including data acquisition, integration, transformation, governance, analytics, and reporting across a diverse portfolio of operational systems and connected technologies.
The organization relies on data to drive key business decisions across operations, finance, human resources, customer engagement, asset management, and strategic planning. This role will transform data from multiple enterprise platforms into a scalable, governed, cloud-based analytics ecosystem that enables trusted insights and empowers business leaders throughout the company.
Success in this position requires a combination of technical leadership, organizational influence, and strategic vision. The VP will lead teams of data engineers and analysts, establish data governance and security standards, partner with executive stakeholders to prioritize investments, and champion a culture centered on data-driven decision making.
Key Responsibilities
Data Platform & Engineering
  • Lead the design, implementation, and ongoing evolution of the enterprise data architecture, leveraging the Microsoft Azure ecosystem, including Azure Data Factory, Azure Data Lake Storage Gen2, Azure Synapse Analytics, Microsoft Fabric, and Power BI.
  • Evaluate emerging technologies and modern data architectures, including lakehouse platforms such as Microsoft Fabric, Databricks, and Snowflake, ensuring scalability and alignment with business objectives.
  • Direct the development of ETL/ELT processes that consolidate data from enterprise applications, including property management, CRM, HCM, financial systems, procurement platforms, and IoT-enabled devices.
  • Establish repeatable and scalable integration frameworks for APIs, third-party data providers, and external system connections.
  • Define enterprise standards for data quality, metadata management, master data management (MDM), and data lineage.
  • Oversee the development of automated CI/CD processes for data pipelines and ensure operational excellence through monitoring, alerting, and service-level management.
Analytics, Reporting & Business Intelligence
  • Lead the enterprise Business Intelligence function and establish best practices around Power BI, including semantic models, workspace governance, row-level security, and self-service analytics capabilities.
  • Partner with functional leaders to design, implement, and monitor meaningful KPIs and performance metrics.
  • Collaborate with Data Science and AI teams to support advanced analytics, predictive modeling, and emerging AI initiatives.
  • Ensure reporting platforms, executive dashboards, and self-service analytics solutions remain accessible, scalable, and governed through a centralized data catalog.
Strategy & Leadership
  • Recruit, mentor, and develop a high-performing team of data engineers, analysts, and BI professionals while fostering accountability, innovation, and continuous learning.
  • Create and maintain a multi-year data and analytics roadmap aligned with business growth initiatives, technology modernization efforts, and future organizational priorities.
  • Serve as a strategic advisor to executive leadership, translating business goals into scalable data and analytics solutions.
  • Manage relationships with cloud providers, analytics vendors, and other technology partners, including contract negotiations and technology evaluations.
Data Governance & Security
  • Develop and implement a comprehensive enterprise data governance framework covering ownership, stewardship, classification, quality standards, and accountability across business domains.
  • Ensure adherence to applicable privacy regulations, industry requirements, and internal security standards.
  • Partner closely with information security teams to maintain role-based access controls, audit capabilities, encryption standards, and governance policies across cloud-based data platforms.
Additional Responsibilities
Additional projects and responsibilities may be assigned as necessary to support organizational goals, strategic initiatives, and operational effectiveness.
Core Competencies
Business Acumen
Demonstrates strong strategic and operational understanding of business functions and leverages that knowledge to drive organizational performance and innovation.
Leadership & Talent Development
Builds high-performing teams through mentorship, coaching, and succession planning while developing future leaders.
Collaboration
Effectively leads cross-functional initiatives and fosters strong partnerships across business and technology organizations.
Communication
Communicates complex technical concepts to both technical and non-technical audiences while driving organizational alignment.
Continuous Improvement
Leads transformational initiatives that improve efficiency, scalability, quality, and business outcomes.
Strategic Thinking
Develops long-term strategies that align technology investments with organizational objectives and market opportunities.
Technical Leadership
Provides executive-level guidance on modern data architecture, analytics platforms, and emerging technologies.
Required Qualifications
  • 12+ years of progressive experience in data engineering, analytics, data architecture, or related disciplines, including at least 4 years in senior leadership roles.
  • Extensive experience with cloud-based data platforms, including the Microsoft Azure ecosystem (Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, Azure Data Lake Storage).
  • Proven expertise designing and managing modern cloud data environments and lakehouse architectures, including Microsoft Fabric, Databricks, and/or Snowflake.
  • Demonstrated success delivering enterprise-scale business intelligence solutions utilizing platforms such as Power BI.
  • Experience integrating data across complex enterprise ecosystems, including ERP, CRM, HCM, property management, and operational systems.
  • Advanced proficiency in SQL and experience with Python or similar scripting languages for data engineering and transformation.
  • Strong background building enterprise data governance programs and large-scale data quality initiatives.
  • Experience leading organizational change initiatives that promote data literacy and enterprise adoption of analytics-driven decision making.
Preferred Qualifications
  • Experience within real estate, multifamily housing, property management, asset management, or similarly complex operational environments.
  • Knowledge of dbt, Microsoft Purview, data catalog solutions, and modern data governance frameworks.
  • Experience working across multiple cloud analytics ecosystems and evaluating tradeoffs among Microsoft Fabric, Databricks, and Snowflake.
  • MBA or advanced degree in Computer Science, Data Science, Information Systems, Analytics, or a related field.
  • Relevant certifications such as Microsoft Azure Data Engineer Associate, Azure Solutions Architect Expert, Databricks certifications, or SnowPro credentials.

This version is cleaner and more marketable for posting externally while maintaining all of the Azure, Fabric, Databricks, Snowflake, Power BI, governance, and executive leadership requirements from the original role.
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