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Product Manager Machine Learning Jobs in Nevada (NOW HIRING)

AI Solutions Architect

Las Vegas, NV

$60.25 - $79.25/hr

Developing account growth strategies, managing pipelines, supporting proposal development, and ... machine learning, deep learning, or generative artificial intelligence solutions in production ...

A strong history of technical work with machine learning software and AI is required In short, you ... Understand business objectives , product and tech roadmaps, requirements, and dependencies * Manage ...

AI Architect

Las Vegas, NV · On-site

$60.50 - $79.75/hr

Build, deploy, and optimize AI and machine learning pipelines using Google Cloud Platform services ... Deploy, orchestrate, and manage AI agents using AgentSpace or similar agent management platforms.

Responsibilities - Leading the design and development of AI and Machine Learning solutions for pharmaceutical clients - Managing complex data analysis to transform raw data into actionable insights ...

New

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... product management, marketing analytics, and healthcare informatics. * Curriculum Awareness ...

Data Science Tutor

Reno, NV · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... product management, marketing analytics, and healthcare informatics. * Curriculum Awareness ...

Data Science Tutor

Henderson, NV · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... product management, marketing analytics, and healthcare informatics. * Curriculum Awareness ...

Data Science Tutor

Las Vegas, NV · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... product management, marketing analytics, and healthcare informatics. * Curriculum Awareness ...

CTIO AI Engineering Manager

Las Vegas, NV · On-site

$73K - $244K/yr

Industry/Sector Not Applicable Specialism IFS - Information Technology (IT) Management Level ... Those in data science and machine learning engineering at PwC will focus on leveraging advanced ...

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Product Manager Machine Learning information

How does a Product Manager specializing in Machine Learning typically collaborate with data scientists and engineering teams?

Product Managers in Machine Learning work closely with both data scientists and engineering teams to translate business objectives into viable AI-driven products. They facilitate communication by defining clear requirements, prioritizing features, and ensuring that the technical roadmap aligns with user needs and company strategy. Regular meetings, progress reviews, and shared documentation are common practices to keep everyone aligned. This cross-functional collaboration is essential for addressing feasibility, optimizing models, and delivering successful products on schedule.

What does a Product Manager for Machine Learning do?

A Product Manager for Machine Learning oversees the development and deployment of machine learning products or features. They work closely with data scientists, engineers, and business stakeholders to identify opportunities where machine learning can deliver value, define product requirements, and guide projects from conception to launch. Their responsibilities include setting the product vision, prioritizing features, ensuring alignment with business goals, and evaluating the impact of machine learning solutions. They also help bridge the gap between technical teams and non-technical stakeholders by translating complex concepts into actionable plans.

What is the difference between Product Manager Machine Learning vs Data Scientist?

AspectProduct Manager Machine LearningData Scientist
Primary FocusOverseeing ML product development, strategy, and deploymentAnalyzing data, building models, and deriving insights
Required SkillsProduct management, ML understanding, cross-functional collaborationStatistics, programming, data analysis
Work EnvironmentProduct teams, engineering, business stakeholdersData analysis teams, research, engineering
Common CertificationsProduct management certifications, ML coursesData science certifications, programming skills

While both roles involve machine learning, Product Manager Machine Learning focuses on guiding ML products from conception to deployment, working closely with engineering and business teams. Data Scientists primarily analyze data and develop models to extract insights. The roles complement each other but differ in their core responsibilities and skill sets.

What are the key skills and qualifications needed to thrive as a Product Manager, Machine Learning, and why are they important?

To thrive as a Product Manager, Machine Learning, you need a solid understanding of product lifecycle management, data analytics, and machine learning concepts—often supported by a technical degree and relevant experience. Familiarity with tools like Python, SQL, JIRA, and machine learning frameworks, as well as certifications such as PMP or Agile, is highly beneficial. Outstanding communication, stakeholder management, and problem-solving skills help you bridge the gap between technical teams and business objectives. These abilities are crucial to successfully guide ML products from ideation to launch, ensuring they deliver real value and align with organizational goals.
What are popular job titles related to Product Manager Machine Learning jobs in Nevada? For Product Manager Machine Learning jobs in Nevada, the most frequently searched job titles are:
What job categories do people searching Product Manager Machine Learning jobs in Nevada look for? The top searched job categories for Product Manager Machine Learning jobs in Nevada are:

Vice President, Artificial Intelligence (VP of AI)

CreditOne

Las Vegas, NV • On-site

Full-time

Posted 27 days ago


Job description

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.

Preferred
Core AI Concepts and Technologies Required
Machine 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.