ZipRecruiter

Log In

1

Data Science Project Manager Jobs in Utah (NOW HIRING)

Bread Financial

Lead Data Scientist

Draper, UT · On-site +1

$144K - $250K/yr

... data science needs within the organization. The Lead Data Scientist is pivotal in generating ... Lead projects as needed or required - (10%) Minimum Qualifications * Bachelor's Degree in ...

Healthcare

Data science software developer

Murray, UT · On-site +1

$124K - $170K/yr

Senior Data Science Software Engineer (Solventum) 3M Health Care is now Solventum At Solventum, we ... To improve the onboarding experience, you will have an opportunity to meet with your manager and ...

Solventum

Data science software developer

Murray, UT · On-site +1

$124K - $170K/yr

Senior Data Science Software Engineer (Solventum) 3M Health Care is now Solventum At Solventum, we ... To improve the onboarding experience, you will have an opportunity to meet with your manager and ...

BYU Pathway

IT Data Scientist

Bluffdale, UT · On-site

$150 - $200/hr

... managers to turn data analysis into actionable algorithms Champion a data-driven culture and help develop best-in-class data science capabilities Create standards and procedures for data science ...

iNovex Information Systems

Data Scientist 2

Ogden, UT · On-site

Job Brief Data Science, Machine Learning, Programming Are you VIGILANT about your career? RealmOne ... Seeking Data Scientist that performs tasks associated with Big Data Platform management, utilizes ...

Boccard

Project Manager

Riverton, UT · On-site

Boccard Life Sciences, industrial integrator located in 35 countries, designs, manufactures ... Competency in MS Suite, ERPs, and other data entry software Travel : Yes, required (up to 40%

Gehc

Service Data Scientist

Salt Lake City, UT · On-site

... data science or cross-disciplinary team on commercially-facing development projects, typically ... Demonstrated awareness of data management methods Demonstrated awareness of real-time analytics ...

nCino OpCo

Senior Data Scientist

Lehi, UT · On-site

$107K - $183K/yr

Understanding of data science priorities and how each contributes to the success of nCino ... Strong experience with SQL or noSQL and database management systems * Experience deploying and ...

Comrise

Project Manager, Engineering

Salt Lake City, UT · On-site

$67.40/hr

Bachelor of Science in Mechanical Engineering, Biomedical Engineering, Materials Science, or a ... project data. * Excellent cross-functional leadership, risk mitigation, and operational problem ...

previous page

Showing results 41-60

All JobsData Science Project JobsData Science Project Manager JobsData Science Project Manager Jobs in Utah

Data Science Project Manager information

See Utah salary details

$15

$52

$73

How much do data science project manager jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for data science project manager in Utah is $52.35, according to ZipRecruiter salary data. Most workers in this role earn between $45.29 and $61.30 per hour, depending on experience, location, and employer.

What is a data science project manager?

A Data Science Project Manager is a professional who oversees and coordinates data science projects from inception to completion. They act as a bridge between technical data science teams and business stakeholders, ensuring that project goals align with organizational objectives. Responsibilities include planning project timelines, managing resources, mitigating risks, and communicating progress. They also help define project requirements, monitor deliverables, and ensure that outcomes meet quality standards. Strong communication, analytical, and organizational skills are essential for this role.

How does a data science project manager typically collaborate with data scientists and stakeholders throughout a project?

A Data Science Project Manager acts as a bridge between technical teams and business stakeholders, ensuring clear communication of goals, timelines, and deliverables. They facilitate regular meetings to discuss project progress, address any obstacles, and realign priorities as needed. By translating business requirements into actionable tasks for data scientists and providing updates to stakeholders, they help ensure that projects stay on track and deliver value. Effective collaboration often involves balancing technical feasibility with business needs, managing expectations, and fostering a cooperative team environment.

What are the key skills and qualifications needed to thrive as a data science project manager, and why are they important?

To thrive as a Data Science Project Manager, you need a solid understanding of data science methodologies, project management principles, and usually a degree in computer science, statistics, or a related field. Familiarity with analytics tools (such as Python, R, SQL), project management software (like Jira or Trello), and certifications such as PMP or Agile/Scrum are often required. Strong leadership, communication, and problem-solving skills set top performers apart by enabling effective team coordination and stakeholder management. These competencies ensure projects are delivered on time, within scope, and generate actionable insights that drive business value.

What is the difference between Data Science Project Manager vs Data Analyst?

AspectData Science Project ManagerData Analyst
Required CredentialsOften requires a bachelor’s or master’s in data science, analytics, or related fields; project management certifications beneficialTypically holds a bachelor’s degree in statistics, mathematics, or related areas; certifications like Microsoft Excel or Tableau are common
Work EnvironmentLeads data science projects, collaborates with data scientists, engineers, and stakeholdersAnalyzes data sets, creates reports, visualizations, and supports decision-making
Employer & Industry UsageUsed in tech, finance, healthcare, and consulting firms managing data science initiativesFound across industries for data reporting, business intelligence, and operational analysis

In summary, a Data Science Project Manager oversees data science projects and manages teams, requiring project management skills and relevant certifications. A Data Analyst focuses on analyzing data and creating reports, with a more technical and analytical role. Both roles are essential in data-driven organizations but differ in scope and responsibilities.

What are popular job titles related to Data Science Project Manager jobs in Utah?

For Data Science Project Manager jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Data Science Project Manager jobs in Utah look for?

The top searched job categories for Data Science Project Manager jobs in Utah are:

What cities in Utah are hiring for Data Science Project Manager jobs?

Cities in Utah with the most Data Science Project Manager job openings:

Data Science Project Manager jobs near you
Infographic showing various Data Science Project Manager job openings in Utah as of August 2026, with employment types broken down into 85% Full Time, 14% Part Time, and 1% Contract. Highlights an 79% Physical, 2% Hybrid, and 19% Remote job distribution, with an average salary of $108,896 per year, or $52.4 per hour.

Sr Data Science Engineer, Discovery

Provo, UT • On-site

Angel Studios
Arts, Entertainment, and Recreation • 51 - 200 employees
Learn more about Angel Studiosexternal

Full-time

Posted 12 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.
Requirements
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