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Vp Of Data Science Jobs (NOW HIRING)

Eccalon LLC

Vice President of Sales

Detroit, MI

... scientific innovation, and engineered solutions into clear customer value. The VP of Sales will ... Analyze sales performance data to identify risks, trends, and growth opportunities. * Ensure ...

BuyWander

VP of Data & AI

Spokane, WA · On-site

$185/hr

Description: VP of Data & AI Location: Seattle, WA (or Remote/Hybrid) Type: Full-Time | Leadership ... We are looking for a data "mad scientist" who is equal parts architect, quant, and business ...

CRH

Vice President, Data & Analytics

Atlanta, GA · On-site

We are the leading provider of innovative outdoor living products and utility infrastructure ... data science. * Foster an agile, outcome-focused culture that can rapidly adapt to changing ...

PHIflow, LLC

VP of Marketing

New York, NY

Vice President of Marketing About PHIflow PHIflow is a data and technology company combining artificial intelligence (AI) and healthcare regulatory expertise to accelerate contract review processes ...

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Vp Of Data Science information

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$41.5K

$142.5K

$201K

How much do vp of data science jobs pay per year?

As of Jun 12, 2026, the average yearly pay for vp of 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 is the 80 20 rule in data science?

In data science, the 80/20 rule suggests that roughly 80% of insights or value often come from 20% of the data or features. Data scientists focus on identifying the most impactful variables or data subsets to optimize model performance and efficiency.

What is the highest paid job in data science?

The highest paid roles in data science are typically senior executive positions such as Chief Data Officer (CDO) or Vice President of Data Science, with salaries often exceeding $200,000 annually. These roles require extensive experience, leadership skills, and expertise in advanced analytics, machine learning, and data strategy.

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

AspectVp Of Data ScienceData Science Manager
ResponsibilitiesStrategic leadership, setting data science vision, overseeing multiple teamsTeam management, project execution, mentoring data scientists
Required CredentialsAdvanced degrees (Master's/PhD), extensive experience in data science and leadershipRelevant experience in data science, leadership skills, often a master's degree
Work EnvironmentExecutive-level, cross-departmental collaboration, strategic planningOperational, project-focused, team management within data science teams

The Vp Of Data Science holds a senior leadership role focused on strategic direction and organizational impact, while a Data Science Manager concentrates on managing teams and executing projects. Both roles require strong technical backgrounds, but the Vp's scope is broader, involving high-level decision-making and cross-functional collaboration.

How much does a VP of data science make?

The salary for a VP of Data Science at major financial institutions like JP Morgan typically ranges from $150,000 to $250,000 annually, with additional bonuses and stock options often included. Compensation varies based on experience, location, and company size, and senior roles may also include performance-based incentives. Strong leadership, advanced analytics skills, and experience with big data tools are common requirements for this position.

What are some common challenges faced by a VP of Data Science when leading cross-functional teams?

A VP of Data Science often navigates challenges such as aligning data science initiatives with business goals, managing expectations across departments, and fostering effective communication between technical and non-technical stakeholders. Balancing the need for innovation with practical deliverables can be complex, especially when integrating data-driven insights into existing business processes. Successful VPs build strong relationships with product, engineering, and executive teams to ensure that data science projects deliver measurable value and support organizational growth.

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

To thrive as a VP of Data Science, you need deep expertise in statistical modeling, machine learning, and data analytics, backed by an advanced degree in a quantitative field and substantial leadership experience. Familiarity with tools such as Python, R, SQL, cloud platforms, and data visualization systems, as well as experience with data governance frameworks, is typically required. Exceptional communication, strategic vision, and the ability to mentor and lead cross-functional teams are vital soft skills in this role. These skills ensure the effective translation of data-driven insights into business strategies and foster innovation and alignment within the organization.

What does a VP of data science do?

A VP of Data Science leads an organization’s data strategy, overseeing teams that develop models, analyze data, and generate insights to support business decisions. They manage data science projects, collaborate with other departments, and ensure the effective use of tools like machine learning and analytics platforms. Strong leadership, technical expertise, and strategic planning are essential for this role.
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Vp Of Data Science jobs near you
Infographic showing various Vp Of Data Science job openings in the United States as of June 2026, with employment types broken down into 8% Internship, 17% Full Time, 67% Part Time, and 8% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.
Vice President, Artificial Intelligence (VP of AI)

Vice President, Artificial Intelligence (VP of AI)

Credit One Bank

Las Vegas, NV

Full-time

Posted 15 days ago

Job description

Position Summary

The Vice President of Artificial Intelligence (VP of AI) will lead enterprise-wide AI strategy, innovation, and execution. This role oversees the development, deployment, and governance of AI/ML systems across the organization, ensuring measurable business value, responsible AI practices, and alignment with corporate strategic goals. The VP of AI collaborates closely with Technology, Data, Operations, Risk, Compliance, and Business Unit leadership to build scalable AI platforms, optimize business processes, and accelerate digital transformation.

Essential Job Functions
  • Define and lead the enterprise AI strategy, including advanced analytics, machine learning, deep learning, and generative AI capabilities.
  • Build and oversee AI Centers of Excellence (CoE) to drive innovation, reusable solutions, and best practices.
  • Partner with IT, Data Engineering, and Cloud teams to establish a scalable AI/ML platform and MLOps frameworks.
  • Identify high-impact AI opportunities that drive automation, operational efficiency, customer experience improvements, and revenue growth.
  • Establish standards for Responsible AI, model governance, explainability, bias detection/mitigation, and regulatory compliance.
  • Lead the development, deployment, and lifecycle management of AI/ML models across multiple business units.
  • Oversee the creation of reusable AI components, annotation processes, model training pipelines, and evaluation frameworks.
  • Implement enterprise-wide generative AI solutions including LLMs, copilots, prompt engineering frameworks, and knowledge automation tools.
  • Collaborate with cybersecurity leaders to implement secure AI architectures, data protection controls, and model threat-defense mechanisms.
  • Promote cross-functional collaboration through transparency, communication, and evangelism of AI capabilities.
  • Build and manage high-performing AI teams including machine learning engineers, data scientists, AI product managers, and researchers.
  • Support annual planning, budgeting, strategic roadmaps, and executive-level presentations for AI programs.
  • Continuously monitor emerging AI trends, tools, and technologies and recommend adoption as appropriate.
  • Perform other duties as assigned.
Position Requirements
  • Bachelor’s degree in computer science, Engineering, Data Science, or related field. Master’s or PhD preferred.
  • 12–15+ years of progressive experience in AI/ML, software engineering, or data science, with 7+ years in leadership roles.
  • Demonstrated experience architecting, deploying, and scaling machine learning or deep learning systems in production.
  • Deep knowledge of Responsible AI frameworks, risk controls, and regulatory expectations.
  • Strong experience with cloud platforms (Azure preferred), distributed systems, and MLOps.
  • Exceptional communication skills, with ability to translate complex AI concepts for senior executives.
  • Proven ability to lead and inspire diverse technical teams.
  • Ability to drive outcomes, influence strategic decisions, and deliver business value.
  • Demonstrated alignment with company values of excellence, ownership, collaboration, and integrity.
PreferredCore AI Concepts and Technologies RequiredMachine Learning & Modeling
  • Supervised, unsupervised, reinforcement learning
  • Deep learning (CNNs, RNNs, Transformers)
  • Natural Language Processing (NLP) & LLMs
  • Generative AI (diffusion models, fine-tuning, RAG)
AI Engineering & MLOps
  • Model training, deployment, monitoring, and retraining
  • Feature stores, vector databases, and model registries
  • CI/CD pipelines for ML (MLOps)
  • GPU/accelerator compute architectures
Cloud & Infrastructure
  • Azure AI, Azure ML, AWS Sagemaker, or Google Vertex AI
  • Kubernetes, containerization, microservices
  • Data platforms (Databricks, Snowflake, Synapse)
Responsible AI & Governance
  • Model explainability (SHAP, LIME)
  • Fairness, bias detection, model risk controls
  • Privacy-preserving ML techniques (differential privacy, federated learning)
Programming & Tooling
  • Python, PyTorch, TensorFlow, JAX
  • LangChain, semantic search, vector embeddings
  • Prompt engineering & LLM orchestration frameworks
Credit One Bank, N.A. is a data-driven financial services company based in Las Vegas. Founded in 1984, Credit One Bank offers a spectrum of credit card products for people in all stages of financial life. Credit One Bank is an equal opportunity employer committed to diversity and inclusion and does not discriminate against any employee or applicant for employment because of age, race, religion, color, disability, sex, sexual orientation, or national origin. Reasonable accommodations can be made for those who require them, including access to job applications and workplace accommodations. Employment at Credit One Bank is based on mutual consent (also known as at-will). This means that employees and the Bank may terminate the employment relationship at any time, with or without cause and with or without notice. Please contact the recruiter for this position to learn more. Credit One Bank does not accept unsolicited resumes from agencies and is not responsible for related fees.