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Causal Inference Machine Learning Postdoctoral Jobs in Orem, UT

VAST Data

Customer Support Engineer

Lehi, UT · On-site

... inference. Designed from the ground up to make AI simple to deploy and manage, VAST takes the cost ... machine learning, object storage, and high-performance computing at customer sites and in the lab.

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Orem, UT

Causal Inference Machine Learning Postdoctoral information

See Orem, UT salary details

$30.9K

$47.1K

$53K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Aug 6, 2026, the average yearly pay for causal inference machine learning postdoctoral in Orem, UT is $47,140.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,500.00 and $49,100.00 per year, depending on experience, location, and employer.

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Orem, UT? For Causal Inference Machine Learning Postdoctoral jobs in Orem, UT, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Orem, UT look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Orem, UT are:
Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Orem, UT as of August 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 100% In-person job distribution, with an average salary of $47,140 per year, or $22.7 per hour.

Solution Architect - Enterprise Systems

ZAGG, Inc.

Midvale, UT • On-site

$59.50 - $78.25/hr

Full-time

Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Zagg rating

7.3

Company rating: 7.3 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

Job description

ZAGG is a global leader in mobile accessories, driving innovation in device protection, power, and productivity through its ZAGG and mophie product lines. With operations in the US, Ireland and China, ZAGG's products are available worldwide at leading retailers. Committed to sustainability, ZAGG is taking steps to reduce its carbon impact in manufacturing, packaging, materials, shipping, and recycling. Our mission is to protect and enhance your mobile lifestyle while making a positive impact on the planet.
As ZAGG's Solution Architect -- Enterprise Systems, you will be the strategic and technical authority connecting ZAGG's business goals to our enterprise technology landscape. You will design scalable, intelligent system architectures, lead complex cross-functional programmes, and ensure every solution is purpose-built to deliver measurable business outcomes - balancing speed-to-market with long-term engineering rigour.
What You Will Do
Business Alignment & Strategic Planning
  • Partner with executive, product, and commercial leadership to translate business objectives into architecture decisions with clear, measurable outcomes.
  • Lead discovery workshops and requirements sessions, producing BRDs and Solution Design Documents that connect every technical decision to a KPI, OKR, or business capability gap.
  • Build and present multi-year technology roadmaps to business stakeholders, articulating investment rationale, risk trade-offs, and expected ROI.
  • Identify where intelligent automation and machine learning capabilities can eliminate operational friction, accelerate revenue, or unlock new business capabilities - and build the business case to pursue them.
  • Facilitate prioritization workshops (MoSCoW, RICE, WSJF) to align stakeholders on scope, sequencing, and return on investment across competing initiatives.
  • Contribute to vendor evaluations and RFPs, assessing enterprise platforms and emerging technology services against business requirements, scalability, and total cost of ownership.

Enterprise Architecture & System Design
  • Design end-to-end architectures across commerce, ERP, CRM, OMS, PIM, supply chain, and data platforms - incorporating intelligent and automated capabilities as core components rather than afterthoughts.
  • Define the intelligent platform layer within the enterprise architecture: the foundational models, data pipelines, orchestration patterns, and API contracts that power data-driven and automated capabilities across the business.
  • Architect solutions leveraging large language models and generative capabilities - including retrieval-augmented pipelines and agentic workflows - connected to enterprise systems via governed, versioned APIs.
  • Lead build vs. buy evaluations for enterprise systems and intelligent capabilities, assessing vendor platforms, foundation models, and custom development against business need and long-term maintainability.
  • Define non-functional requirements across the full stack: availability SLAs, disaster recovery targets (RTO/RPO), security posture, model performance baselines, and compliance obligations (GDPR, CCPA, SOX, EU AI Act).
  • Produce and maintain Architecture Decision Records (ADRs), capability maps, system-of-record designations, and architectural diagrams as the authoritative source of truth.

Integration & Data Strategy
  • Architect enterprise integrations using scalable middleware and iPaaS platforms (MuleSoft & other platforms) and event-driven messaging (AWS, SQS) - extended to support intelligent data ingestion, model feedback loops, and real-time inference.
  • Define the data strategy required to power intelligent capabilities: feature stores, vector databasesreal-time streaming pipelines, and lakehouse architecture (Snowflake, Databricks, BigQuery).
  • Establish master data management and data governance standards ensuring all systems - including those powering automated and predictive capabilities - are built on accurate, consistent, and trustworthy data.
  • Own API strategy across enterprise systems and intelligent services: versioning standards, lifecycle management, security controls (OAuth 2.0, mTLS), and developer experience including interfaces for language model and agent-to-system communication.

Platform Governance & Best Practices
  • Establish and enforce technology standards, integration patterns, and usage policies - including deployment approvals, responsible use principles, explainability requirements, and bias monitoring for automated decision-making systems.
  • Run formal governance processes for major platform changes and intelligent system releases: performance benchmarking, data lineage review, security sign-off, and stakeholder approval before production deployment.
  • Define and track platform health metrics across enterprise and intelligent systems: uptime, API latency, integration error rates, model performance (accuracy, drift, latency), and technical debt - with regular reporting to technology leadership.
  • Lead security architecture reviews ensuring all enterprise systems meet internal policy, data privacy regulation, and audit requirements.
  • Champion operational excellence: alerting, monitoring, and incident response standards covering enterprise integrations and automated system behaviour in production.

Technical Leadership & Delivery
  • Mentor engineers and data practitioners, conduct architecture reviews, and build capability across technology and business teams - fostering a culture where intelligent tooling is a practical, well-governed asset.
  • Collaborate with Project and Program Managers to align program scope with commercial budgets, timelines, and business priorities.
  • Define success metrics for every program- including performance baselines, business impact KPIs, and operational cost benchmarks - with measurement frameworks live from go-live.
  • Serve as the technical escalation point for complex system issues, integration failures, and automated system behavior concerns in production.

What You'll Need to Be Successful
Required Qualifications
  • 7+ years in enterprise solution architecture, technical consulting, or senior engineering leadership with a track record of large-scale system delivery.
  • Proven hands-on experience designing and deploying intelligent or ML-driven solutions in production - including language model integration, retrieval-augmented generation, or model operationalization within enterprise environments.
  • Solid experience integrating across two or more enterprise platforms: ERP (SAP, Oracle, NetSuite), CRM (Salesforce, Microsoft Dynamics), OMS, PIM, eCommerce, and data warehouses.
  • Deep knowledge of enterprise integration patterns: REST and GraphQL APIs, event-driven architecture, message queuing, and iPaaS platforms (MuleSoft, Boomi, or similar).
  • Practical experience with cloud intelligence services (AWS Bedrock, Azure OpenAI, Google Vertex AI) and direct integration with foundation model providers.
  • Familiarity with orchestration frameworks, vector databases, and prompt engineering practices at production scale.
  • Strong command of cloud infrastructure (AWS, GCP, or Azure), containerisation (Docker / Kubernetes), and CI/CD pipeline design.
  • Demonstrated ability to translate business strategy into technology roadmaps and communicate trade-offs clearly to C-suite and board-level audiences.
  • Experience producing high-quality documentation: BRDs, SDDs, ADRs, capability maps, and executive-level presentations.

Preferred Qualifications
  • Experience building agentic systems: multi-agent orchestration, tool-use patterns, and human-in-the-loop workflow design.
  • Background in consumer products, retail, or eCommerce with exposure to use cases such as demand forecasting, personalisation engines, or intelligent customer service.
  • Experience with MLOps platforms and production model monitoring for drift, bias, and performance degradation.
  • Familiarity with the EU AI Act and responsible-use principles including explainability, fairness, and auditability.
  • Experience with TOGAF, Zachman, or another enterprise architecture framework.
  • Relevant certifications: AWS Solutions Architect Professional, Google Professional Cloud Architect, Azure Solutions Architect Expert, TOGAF 9/10, or an ML specialist certification.

What You'll Love About ZAGG
  • Generous PTO - Plus two floating holidays to use as you choose.
  • 401(k) Match - Company contributions to support your long-term financial goals.
  • Employee Product Perks - Hands-on access to ZAGG's full portfolio of industry-leading products.
  • Growth Culture - Join a team with strong year-over-year momentum and a clear path to expanded responsibility.

ZAGG is an Equal Opportunity Employer. We welcome and encourage diversity in the workplace. We do not discriminate in any aspect of employment on the basis of race, color, religion, national origin, ancestry, gender, sexual orientation, gender identity and/or expression, age, veteran status, disability, or any other characteristic protected by federal, state, or local employment discrimination laws where Zagg does business.
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