Top Machine Learning Jobs in Provo, UT

As a Senior Data Scientist, you will apply machine learning and AI algorithms, conduct feature engineering, and collaborate with teams to optimize models and provide data insights.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

As a Data Scientist, you'll leverage machine learning and AI to analyze data, build models, and develop intelligent services, collaborating with various stakeholders.

Lead delivery of advanced analytics and investigative support for federal programs. Manage multiple concurrent projects, schedules, costs, risks, personnel, vendors, and stakeholder communications to ensure quality, timely contract-aligned deliverables.

Lead the implementation of AI-driven supply chain solutions, collaborating with clients to enhance their business processes and ensure seamless technology integration.

Lead strategy and roadmap for DTNs upstream intelligence portfolio, especially Farm Intelligence. Conduct customer discovery, define product metrics, prioritize features, and partner with Engineering, Design, Data Science, and Commercial teams to deliver data-intensive, AI-enabled B2B SaaS products. Drive launches, adoption, and continuous optimization through experimentation, analytics, and stakeholder alignment.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

Lead partner marketing with consulting and systems integrators to drive joint GTM plans, co-marketing programs, repeatable partner collateral, customer stories, partner communications, and measurable pipeline growth.

Design and run partner presales enablement programs that build hands-on technical proficiency across ISV and C&SI partners. Own curriculum, assessments, roadmaps, KPIs, and large-scale learning events; coach trainers; align cross-functionally to drive partner-sourced pipeline and certification outcomes.

Define and execute Americas logistics and warehousing strategy; lead multi-site DC operations, inbound/outbound logistics, carrier management, trade compliance/imports, P&L oversight, WMS/TMS/ERP optimization, and AI/automation adoption. Requires cross-regional enablement and ~25-40% travel.

Lead operationalization of AI strategy by implementing scalable AI platforms, standards, and governance. Identify and prioritize high-impact AI and analytics use cases, drive end-to-end delivery into production, embed AI into business processes, manage AI teams, mentor practitioners, and evaluate emerging AI/GenAI technologies to ensure responsible, secure, and measurable business impact.

Lead the architecture, implementation, integration, and optimization of Oracle EPM Planning solutions. Design planning, budgeting, forecasting, modeling, security, metadata, and reporting frameworks; configure Oracle EPM modules; and apply AI-driven forecasting capabilities. Lead requirements workshops, testing, deployment, stakeholder presentations, and pre-sales activities. Mentor consultants and developers while advising finance and executive stakeholders on enterprise planning transformation initiatives.

Design and operate large-scale, AI-native cloud infrastructure (especially Kubernetes/AKS). Improve service quality, automate incident prevention/remediation, accelerate engineering productivity, and integrate AI tools into development workflows.

The Product Marketing Manager will develop go-to-market strategies, create compelling messaging, perform market research, and collaborate with product and sales teams to position Oscilar's AI risk platform effectively.

Identify business challenges and translate them into AI initiatives; architect AI solutions using appropriate models, frameworks, and infrastructure; guide data science and engineering teams through proof-of-concept development and deployment; mitigate bias and model drift through monitoring; and communicate an AI vision aligned with business goals.

Develop and optimize scalable, low-latency bot threat detection, request risk detection, and fraud prevention products. Responsibilities include implementing features, improving existing functionality, designing architectures, testing and deploying code, troubleshooting complex customer issues with Level 3 Support, and analyzing web and mobile platform data. The role requires performant web API development, knowledge of HTTP and common data formats, and integration of machine learning tools and LLMs.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

You will execute and improve insurance workflows, analyze operational efficiency, collaborate with product teams, and support onboarding and documentation efforts.

Lead and deliver complex, cross-team AI product programs, drive agent-first SDLC adoption, ensure integration and operational readiness of AI workflows, define metrics, communicate risks and status, and standardize TPM best practices across Product and Engineering.

Analyze microbiome and longitudinal clinical data, develop biomarkers and microbiome metrics using statistical and machine learning methods, and create visualizations, reports, protocols, and study reports. The role supports research, product development, scientific publications, proposals, and clinical studies while collaborating independently with microbiome experts in a remote-first startup environment.

Adjudicate automated vessel detections humans cant resolve: identify dark/spoofed/low-confidence contacts, investigate AIS/GNSS manipulation and ownership, produce sourced identification records, and supply labeled ground truth to improve detection models.

Manage the development and implementation of Evio Compass, an analytic product identifying patient risks, while guiding cross-functional teams to drive customer success and enhance ML/AI product features.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.