Top Senior Machine Learning Jobs in Las Vegas, NV

Own Fetch’s on-platform advertising measurement strategy, including event taxonomies, attribution, incrementality, reporting, and data pipeline standardization. Lead migration to standardized ad event infrastructure, define measurement methodologies such as multi-touch attribution and geo lift, and partner with engineering, data science, advertisers, and third-party measurement providers. Establish scalable measurement standards and ensure new ad products are instrumented and attributable from launch.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Leads data science and advanced analytics initiatives for Auto Claims by developing machine learning and predictive modeling algorithms, integrating structured and unstructured data, and creating tools to support business decisions. Communicates modeling results to stakeholders, researches emerging techniques, manages moderately complex projects, and provides mentorship to junior data scientists. The role is remote with up to 10% travel to regional offices.

Advise airport-industry customers on GIS solutions, demonstrate ArcGIS capabilities, and support account teams with technical sales strategies. Design solutions that align Esri technology with customer challenges, provide deployment and administration best practices, troubleshoot system architectures, and present at conferences. The role requires strong GIS, cloud, geodatabase, consulting, communication, and presentation skills, along with 25–50% domestic or international travel.

Leads enterprise Key Drivers Analytics and an Associate Director team delivering advanced analytics across pharmaceutical business priorities. Owns analytics roadmaps, methodologies, governance, modeling standards, talent deployment, and globally distributed delivery. Directs multivariate analyses, segmentation, diagnostics, scenario modeling, validation, insight generation, and executive recommendations. Partners cross-functionally to translate evidence into measurable business actions while ensuring reproducibility, compliance, responsible AI, and scalable analytical solutions.

Leads enterprise advanced analytics and modeling to identify drivers of business performance across brands, markets, customers, accounts, and geographies. Translates findings into prioritized actions, playbooks, launch strategies, and scenario-based recommendations. Oversees rigorous, scalable analytical approaches, partners with senior stakeholders, and develops analytics talent across the organization.

Lead strategic sales of AI Workforce solutions, including Microsoft Copilot, AI agents, Viva, Windows 365, and Azure Virtual Desktop. Develop customer strategies, identify opportunities, build pipeline, conduct consultative business and technical conversations, and orchestrate internal teams and partners to close complex deals. The role requires expertise in technology sales, AI, forecasting, demand generation, stakeholder engagement, competitive positioning, and cross-selling across enterprise and regulated customers.

Lead analyst relations programs to improve Databricks’ positioning in industry evaluations, including Gartner, Forrester, and IDC. Develop differentiated narratives, submissions, demonstrations, customer evidence, and executive briefings. Scale analyst relations through AI-assisted workflows, convert analyst feedback into product and go-to-market recommendations, and influence product, marketing, sales, and executive teams. Manage multiple deadline-driven workstreams while improving analyst advocacy, market visibility, and evaluation outcomes.

Develop and support scalable Python services, APIs, and event-driven systems for digital freight pricing and booking. Build experimentation frameworks, AI-assisted pricing harnesses, validation guardrails, real-time platforms, and machine-learning integrations. Lead architecture decisions, ensure security and reliability, mentor engineers, and collaborate across product, operations, revenue management, data science, and engineering teams.

Own the commercial success of a portfolio of strategic enterprise accounts, including renewals, retention, expansion revenue, forecasting, and multi-year account strategies. Build executive relationships, lead business reviews and complex negotiations, coordinate cross-functional resolution efforts, and represent client needs internally. Mentor other Account Managers and use generative AI tools for account planning and communications while protecting confidential information.

Coordinate and govern release cycles for the HIGLAS Product Suite within an IT service management environment. Responsibilities include managing release phases and tasks, maintaining release calendars, conducting quality checks, tracking risks and dependencies, presenting status to review boards, coordinating outage notifications, completing post-release reviews, facilitating cross-functional meetings, and maintaining procedures, reports, dashboards, and KPIs. The role also supports process improvement and AI-enabled governance workflows.

Owns strategy, roadmaps, backlogs, and end-to-end delivery for business automation and AI products. Partners with engineering and executives to define requirements, launch reliable features, evaluate build-versus-buy decisions, manage budgets and cloud/API costs, measure ROI, and maintain product performance. Requires strong technical, agile delivery, analytics, and healthcare privacy expertise, with potential progression into product management leadership.

Define and scale autonomous agricultural machinery products through customer research, experimentation, roadmap prioritization, system requirements, and cross-functional delivery. Partner with engineering and commercial teams on hardware, embedded software, computer vision, machine learning, navigation, controls, and mobile solutions. Use data, field feedback, and performance metrics to improve adoption, customer confidence, mission quality, and product impact.

Own enterprise sales across new and existing accounts, leading consultative, ROI-driven sales cycles from discovery through close. Develop account plans, build executive relationships, collaborate with Solutions and Forward Deployed Engineers, forecast pipeline, maintain CRM discipline, lead business reviews, represent the company at industry events, and relay customer insights to shape the product roadmap.

Develop graduate-level Applied AI course materials, including lessons, labs, case studies, assignments, rubrics, projects, readings, templates, and videos. Design practical activities using LLMs, RAG, AI agents, automation, APIs, and evaluation tools. Collaborate with learning designers and university faculty, explain complex concepts to nontechnical learners, maintain academic quality, and update content as AI tools and regulations evolve.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads technology consulting engagements involving technology, data, and AI strategy; software and architecture assessments; analytics and business intelligence; program delivery; business cases; product roadmaps; and due diligence. Manages client stakeholders, workstreams, deliverables, priorities, and junior team members while developing strategic insights, resolving complex issues, supporting sales, and delivering large-scale technology programs.

Drives business transformation and strategic change initiatives by developing transformation strategies, improving processes and organizational design, managing change adoption, engaging senior stakeholders, coordinating cross-functional projects, delivering training, and tracking benefits realization. The role manages transformation projects from planning through post-implementation review while ensuring alignment with business objectives, timely delivery, budget adherence, and successful organizational adoption.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

Leads AML transaction monitoring optimization, including rule testing, threshold tuning, scenario design, and alert reduction. Drives FIU automation and workflow improvements, evaluates AI-enabled compliance tools, and partners with Product, Engineering, Data, Operations, and vendors on monitoring infrastructure and integrations. Supports scalable compliance controls across new products, jurisdictions, and transaction volumes while maintaining regulatory alignment and detection effectiveness.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Develops data quality checks, metadata standards, and AI use-case inventories for HHS. Cleans and integrates complex datasets, applies machine learning and NLP for automated tagging and classification, evaluates AI governance risks, and produces reporting datasets, dashboards, and recommendations. The role requires translating ambiguous federal policy and reporting requirements into repeatable analytical workflows while collaborating with product, design, engineering, and government stakeholders.

Develop and deploy machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, optimizing prompting, embeddings, RAG, and fine-tuning techniques, designing evaluation methodologies, and integrating models into scalable production systems. The role requires collaboration with cybersecurity researchers, software engineers, data engineers, and product managers.

Own the authorization optimization product roadmap, improving legitimate transaction approval rates and recovered merchant revenue. Define technical requirements, APIs, data models, integrations, and issuer-specific decisioning strategies. Partner with engineering, data science, sales, solutions, and customer success to deliver fraud-aware authorization improvements involving retries, 3DS, decline recovery, and payment routing. Manage partnerships with payment ecosystem providers, quantify opportunities using issuer and BIN intelligence, and communicate product strategy and performance to stakeholders.

Leads the design, implementation, and support of complex, scalable software components and distributed systems. Provides technical leadership through architecture decisions, mentoring, code and design reviews, troubleshooting, and engineering standards. Partners with product, quality, architecture, and DevOps teams on secure cloud-native solutions, CI/CD, observability, performance, releases, and platform modernization. Participates in hiring and communicates technical risks and tradeoffs to stakeholders.