ZipRecruiter

Log In

1

Modeling Jobs in Provo, UT (NOW HIRING)

The David Movie

Data Scientist

Provo, UT · On-site +1

$110K - $130K/yr

Gone is the old model where the deepest pockets pick the stories we share. Angel restores choice to our 2 million guild members who decide what we produce, what we take to theaters, and most ...

Pattern

International Strategy & Ops Manager

Lehi, UT · On-site +1

$112K - $150K/yr

Develop integrated operating model strategies and assess business challenges, needs, and gaps. Analyze business performance, market trends, and operational data to identify growth opportunities ...

Pattern

International Strategy & Ops Manager

Lehi, UT · On-site

$112K - $150K/yr

Develop integrated operating model strategies and assess business challenges, needs, and gaps. Analyze business performance, market trends, and operational data to identify growth opportunities ...

Pattern

International Strategy & Ops Manager

Lehi, UT · Hybrid

$112K - $150K/yr

Develop integrated operating model strategies and assess business challenges, needs, and gaps. Analyze business performance, market trends, and operational data to identify growth opportunities ...

Mobettahs

Senior Financial Analyst - Real Estate

Lehi, UT · On-site

$80K - $100K/yr

The ideal candidate brings strong financial modeling skills alongside hands-on experience with real estate analytics platforms. You'll be as comfortable building a market-entry pro forma as you are ...

Crew Career Center

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

Language Model (LM) Development & Fine-tuning: * Research, select, and experiment with appropriate open-source Language Models (Large & Small) (e.g., Phi-3, Mistral, Llama, Nemotron-H families) for ...

Waystar

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

Language Model (LM) Development & Fine-tuning: * Research, select, and experiment with appropriate open-source Language Models (Large & Small) (e.g., Phi-3, Mistral, Llama, Nemotron-H families) for ...

Patientco

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

Language Model (LM) Development & Fine-tuning: * Research, select, and experiment with appropriate open-source Language Models (Large & Small) (e.g., Phi-3, Mistral, Llama, Nemotron-H families) for ...

JourneyTEAM

Business Intelligence Developer

Sandy, UT · On-site +1

$80K - $150K/yr

Develop robust semantic models-including Direct Lake models on Microsoft Fabric-with well-structured DAX measures, calculation groups, and row-level security. * Work directly with clients to gather ...

Cisco

Applied AI Scientist

Lehi, UT · On-site

Join the Foundational Modeling team at Splunk, where we advance the state of AI for highvolume, realtime, multimodal machinegenerated data - including logs, time series, traces, and events. We ...

JOURNEYTEAM

Business Intelligence Developer

Sandy, UT · On-site

Develop robust semantic models-including Direct Lake models on Microsoft Fabric-with well-structured DAX measures, calculation groups, and row-level security. * Work directly with clients to gather ...

previous page

Showing results 41-60

All JobsModeling JobsModeling Jobs in Provo, UT

Modeling information

See Provo, UT salary details

$20

$38

$72

How much do modeling jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for modeling in Provo, UT is $38.15, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $41.15 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in modeling, and why are they important?

To excel in Modeling, individuals need physical fitness, confidence, a strong sense of body awareness, and often a portfolio of professional photos; previous experience or training in runway walking or posing is also beneficial. Familiarity with industry tools such as digital comp cards, booking platforms, and sometimes contracts or modeling agency agreements is typically required. Exceptional communication skills, punctuality, and the ability to adapt to feedback help models stand out in this highly competitive field. These qualifications and personal attributes are essential for representing brands effectively and securing ongoing work in various modeling environments.

How do you get into modeling?

To get into modeling, individuals typically build a portfolio of professional photos, gain experience through local or online agencies, and attend open casting calls or submit applications to modeling agencies. Having a good appearance, understanding industry standards, and maintaining a healthy lifestyle can also improve chances of success.

What is modeling?

A modeling job involves showcasing clothing, products, or concepts for advertisements, fashion shows, or promotional events. Models work in various industries such as fashion, commercial, fitness, and art. They pose for photographers, walk on runways, or demonstrate products for brands. Success in modeling often requires a strong portfolio, good physical presentation, and professional networking.

How much money does modeling pay?

Modeling pay varies widely depending on experience, market, and type of modeling. Entry-level models may earn a few hundred dollars per day, while experienced or high-profile models can earn thousands per day or more. Many models work on a freelance basis, and income can fluctuate based on assignments and demand.

What types of modeling assignments can I expect, and how does the work schedule typically look?

Modeling assignments can range from fashion runway shows and editorial photo shoots to commercial advertisements, product promotions, or fitness campaigns. The work schedule is often variable, with early mornings, late evenings, or weekend commitments depending on the client’s needs, and assignments may be short-term or ongoing. Models frequently work with photographers, stylists, makeup artists, and directors, requiring strong teamwork and adaptability. Flexibility and professionalism are key, as job notifications can come at short notice and may require travel. Building a diverse portfolio and positive industry relationships can open doors to more consistent and high-profile opportunities.

Is 25 too late to start modeling?

Modeling is a flexible industry where individuals of various ages can find opportunities, especially in commercial, plus-size, or mature markets. While many models start in their teens, some successful models begin in their late twenties or later, and building a strong portfolio and relevant skills can improve chances regardless of age.
What are the most commonly searched types of Modeling jobs in Provo, UT? The most popular types of Modeling jobs in Provo, UT are:
What cities near Provo, UT are hiring for Modeling jobs? Cities near Provo, UT with the most Modeling job openings:
Modeling jobs near you
Infographic showing various Modeling job openings in Provo, UT as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $79,362 per year, or $38.2 per hour.

Data Scientist

The David Movie

Provo, UT • On-site, Remote

$110K - $130K/yr

Full-time

Re-posted 2 days ago

Job description

Angel is changing the future of entertainment and is one of the fastest-growing distributors. Our rapidly expanding library of light-amplifying stories has grown 10x in under 2 years. Gone is the old model where the deepest pockets pick the stories we share. Angel restores choice to our 2 million guild members who decide what we produce, what we take to theaters, and most importantly what parents bring to their homes. Check out angel.com/watch
You'll take ownership of how we understand user engagement and behavior, build predictive models, and create the data foundation that powers personalized discovery for millions of members. From recommendation systems to the metrics that drive our strategy, you'll turn data into insights and insights into action-shaping how fans discover and fall in love with Angel's ever-growing library of stories.
Why Join Angel
Angel Studios is growing fast. Our content library has expanded 10x in under two years, and over two million Guild members now decide what gets produced, funded, and watched. As that library grows, the gap between what members would love and what they actually find is the most important problem on the platform.
You'll be the first dedicated data scientist on Discovery. You'll own the analytical foundation that makes our recommendation system measurable, improvable, and eventually intelligent. Today, our recommendations run on AWS Personalize. Your work will determine how far that takes us and when we've outgrown it.
This is a data science role, not an ML engineering role. Day one is about analytical rigor: metrics, experimentation, causal inference, and making the team smarter about our members. But we're building toward a future where Angel owns its recommendation models end to end. If you're a strong data scientist who wants to grow into owning models in production, this is the role where that trajectory is real and supported.
What You'll Own
  • Metrics and measurement. Define, instrument, and maintain the Discovery metrics framework across web, mobile, and TV. Model metrics (precision, recall, coverage, diversity), customer metrics (CTR, playthrough, completion, session depth, cold-start ramp time), and business metrics (retention segmented by recommendation engagement). You decide what we measure, how we measure it, and when a metric is lying to us.
  • Experimentation. Own the A/B testing and experimentation pipeline for Discovery surfaces. Design experiments with statistical rigor: sample sizing, duration, segmentation, guard-rail metrics. Build the institutional muscle so the team ships with evidence, not opinions. We use GrowthBook.
  • User behavior analysis. Decode how members discover, browse, and engage with content across three very different platforms. Identify patterns in Guild voting, theatrical-to-streaming conversion, content affinity, and churn risk. Surface the insights that change how the product team thinks about the problem.
  • Causal inference. Distinguish correlation from causation in engagement data, where selection bias is everywhere. When recommendation engagement correlates with retention, determine whether the system is driving retention or whether high-intent users are simply more likely to click. Design quasi-experiments when randomization isn't feasible.
  • Data foundations for analytics. Build and maintain the dbt models, data pipelines, and analytical infrastructure that make data accessible and trustworthy for the Discovery team and the broader organization. If the data is wrong, nothing else matters.
Where This Role Grows
The trajectory from data scientist to ML engineer on this team is explicit, not aspirational. As the analytical foundation matures, the work shifts:
  • Feature engineering for recommendations. Evaluate which new signals (voting history, explicit ratings, content metadata, theatrical engagement) improve recipe performance in AWS Personalize. Graduate from analyzing features to building them.
  • Model prototyping and evaluation. Prototype recommendation approaches (content-based filtering, hybrid models, embeddings) and evaluate them against the golden eval set you built in your first months.
  • Owning a model from experimentation to deployment. When the team outgrows Personalize, you'll take a model from notebook to production: writing testable Python, managing data lifecycles (pipelines, feature stores, monitoring, retraining), and thinking about systems design (latency, failure modes, observability).

The timing of this transition depends on the work, not a calendar. You won't be pushed into model building before the foundation is solid, and you won't be held back once it is.
What You Bring
    • Statistical rigor. You design experiments correctly: power analysis, multiple comparisons, confidence intervals, Bayesian methods where appropriate. You can explain to a non-technical stakeholder why a result is or isn't significant.
    • Causal inference chops. You've worked with observational data where naive correlations are misleading. Familiar with propensity score matching, difference-in-differences, instrumental variables, or regression discontinuity. You know when to reach for them.
    • SQL and Python fluency. SQL is your first language for data exploration. Python for analysis, modeling, and automation. Your code is clean enough that someone else can read it six months later.
    • Experimentation design and analysis. You've designed, run, and analyzed A/B tests in production. You understand interaction effects, novelty effects, and Simpson's paradox.
    • Communication. You translate complex analysis into clear narratives. Stakeholders trust your conclusions because you show your reasoning, name your assumptions, and flag what you don't know.
    • Data modeling. Experience with dbt or equivalent transformation frameworks. You've built analytical data models that other teams actually use.

    Signals you're on the MLE trajectory
    Not requirements for day one, but what tells us you'll grow into model ownership:
    • You write Python like a software engineer, not just a notebook user: tests, packaging, code reviews.
    • You've thought about what happens after an analysis becomes a model: data pipelines, feature generation, monitoring, retraining.
    • You're curious about systems design for ML features: latency, throughput, failure modes, observability.
    • You've touched some part of the lifecycle around a deployed model, even if it wasn't your primary job.
Experience
  • 6+ years as a data scientist or senior analytical role.
  • Experience with large-scale user engagement and behavior data. Streaming, entertainment, marketplace, or consumer subscription domains preferred.
  • Track record of defining metrics frameworks that stakeholders actually adopted.
  • Familiarity with modern data tools: dbt, data warehousing (Snowflake, BigQuery, Redshift), experimentation platforms (GrowthBook, Optimizely), BI tools (Rill, Looker).
  • Experience with recommendation systems or personalization is a strong plus, not a prerequisite.
  • Familiarty with AI
The Problem Space
  • A catalog of roughly 1,100 titles that has grown 10x in two years, with heavy top-title concentration. The top 10% of titles drive the majority of watch hours. Discovery needs to surface the long tail.
  • Three platforms (TV, mobile, web) with starkly different engagement patterns. TV drives the highest engagement but is mostly single-title sessions. Mobile has more browsing behavior. Web is underserved.
  • A recommendation system (AWS Personalize) that shows strong retention signal for engaged users but has significant precision and coverage gaps to close.
  • A unique data asset in Guild voting behavior. Members vote on what gets produced and funded before they ever watch it. That signal may be the most differentiated input the recommendation system has.
  • A content model unlike general streaming: faith-friendly, owned IP, theatrical-to-streaming pipeline. What "good discovery" means here is genuinely different from Netflix or Spotify.
What Success Looks Like
In the first six months, the Discovery team has a metrics framework they trust and use weekly, the experimentation pipeline is running and producing confident results, and you've surfaced at least one behavioral insight that changed how the team prioritized its roadmap. By year one, you're contributing to feature engineering for recommendations, you've prototyped and evaluated at least one model improvement, and the team has a data-informed view of when Personalize is sufficient and when custom models are warranted.
Work environment: Hybrid team members must have a private and quiet area for working hours in their location. When in the main office, expect a comfortable, air-conditioned work environment. Team members are issued their own desks, but the office is an open, shared space and can be fast-paced and occasionally noisy.
Physical demands: Will need to be able to sit or stand at a desk for extended periods of time.
Position type and expected hours of work: Regular full-time, 40 hours per week.
Travel required: 2-4 onsite events in Utah each year. There may be other opportunities to travel, but no other significant out-of-state travel is anticipated
Work authorization: Must be authorized to work in the United States.
EEO statement: At Angel Studios, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and teammates.
Other duties: Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the team member for this job. Duties, responsibilities and activities may change at any time with or without notice.

Apply