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Remote Bayesian Jobs in Utah (NOW HIRING)

Audiohook

Data Scientist

Eden, UT · On-site +1

Communicate clearly and proactively in a remote-first environment Qualifications Required ... Experience with Bayesian methods or Bayesian MMM frameworks (e.g., PyMC-Marketing, LightweightMMM ...

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Remote Bayesian information

What are the key skills and qualifications needed to thrive as a Remote Bayesian, and why are they important?

To thrive as a Remote Bayesian, you need strong statistical knowledge, expertise in Bayesian inference, and a background in mathematics or data science, often supported by an advanced degree. Familiarity with programming languages like Python or R, Bayesian software such as Stan or PyMC, and experience with remote collaboration tools are typically required. Critical thinking, problem-solving, and clear communication are essential soft skills for interpreting results and working with distributed teams. These abilities are vital for delivering accurate, actionable insights in a remote environment where clear analysis and collaboration drive project success.

What is the difference between Remote Bayesian vs Remote Data Scientist?

AspectRemote BayesianRemote Data Scientist
Required CredentialsBackground in statistics, Bayesian methods, programming (Python/R)Statistics, computer science, or related degree; programming skills
Work EnvironmentResearch-focused, analytical tasks, often in tech or financeData analysis, modeling, business insights across industries
Industry UsageResearch institutions, AI, machine learning, financeTech companies, consulting, finance, healthcare

Remote Bayesian specialists focus on Bayesian statistical methods and probabilistic modeling, often in research or AI contexts. Remote Data Scientists have broader roles in data analysis and modeling across various industries. While both roles require strong analytical skills and programming, Remote Bayesian roles emphasize Bayesian techniques, whereas Remote Data Scientist roles encompass a wider range of data analysis tasks.

What is a Remote Bayesian?

A Remote Bayesian is a professional who specializes in Bayesian statistics and probabilistic modeling while working remotely, often in fields like data science, machine learning, or research. They use Bayesian methods to update probabilities and make predictions based on data, collaborating with teams through digital communication tools. Remote Bayesians may work for tech companies, research institutions, or as independent consultants, applying their expertise to solve complex problems without being tied to a physical office location.

How do Remote Bayesian professionals typically collaborate with cross-functional teams given the virtual nature of their work?

Remote Bayesian professionals often work closely with data scientists, engineers, and decision-makers through virtual collaboration tools such as video conferencing, shared code repositories, and project management platforms. Clear communication is key, as they must explain complex probabilistic models and inferences to both technical and non-technical stakeholders. Regular check-ins and documentation help ensure alignment on project goals, data requirements, and model outcomes. This collaborative dynamic fosters an environment where insights from Bayesian analysis can directly inform business or research decisions, despite the physical distance.
What are the most commonly searched types of Bayesian jobs in Utah? The most popular types of Bayesian jobs in Utah are:
What are popular job titles related to Remote Bayesian jobs in Utah? For Remote Bayesian jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Remote Bayesian jobs in Utah look for? The top searched job categories for Remote Bayesian jobs in Utah are:
What cities in Utah are hiring for Remote Bayesian jobs? Cities in Utah with the most Remote Bayesian job openings:
Remote Bayesian jobs near you

Data Scientist

Audiohook

Eden, UT • On-site, Remote

Full-time

Medical, Dental, Vision, PTO

Re-posted 6 days ago

Job description

Role Overview

The Data Scientist will own the measurement science behind Audiohook\'s performance audio advertising platform. You\'ll design and run incrementality tests, build and maintain marketing mix models, and apply causal analysis to quantify how Audiohook drives outcomes for advertisers. This role combines hands-on modeling with the opportunity to shape how we prove value to customers, sharpen our bidding and optimization systems, and influence product direction. You\'ll collaborate closely with Engineering, Product, Sales, and Customer Success to ensure measurement isn\'t just statistically sound but operationally useful.

Key ResponsibilitiesMarketing Measurement & Causal Inference

  • Design and run incrementality experiments (geo, ghost bidding, holdout, PSA) that quantify Audiohook\'s lift for advertisers

  • Build, maintain, and evolve marketing mix models (MMM) and multi-touch attribution analyses across customer campaigns

  • Apply causal inference methods — difference-in-differences, synthetic controls, instrumental variables, propensity scoring — to questions that can\'t be answered with RCTs

  • Translate measurement results into clear narratives for advertisers, internal stakeholders, and the product team

Modeling & Analysis

  • Partner with Engineering on the data and modeling layer that powers bidding, pacing, and optimization decisions

  • Develop and validate predictive models that improve campaign performance and platform efficiency

  • Instrument experiments and analyses for reproducibility, monitoring, and ongoing measurement quality

Cross-Functional Collaboration

  • Partner with Sales and Customer Success on measurement studies for priority accounts and renewals

  • Partner with Product on roadmap inputs grounded in causal evidence, not just descriptive data

  • Present findings to advertisers, internal teams, and leadership in clear, decision-ready formats

  • Communicate clearly and proactively in a remote-first environment

QualificationsRequired

  • Bachelor\'s or Master\'s degree in Statistics, Economics, Data Science, Computer Science, or related quantitative field

  • 3–5 years of applied data science experience with a focus on marketing measurement — incrementality, MMM, attribution, or causal analysis

  • Hands-on experience designing and analyzing experiments (A/B, geo, holdout) in a marketing or advertising context

  • Strong fluency in Python (pandas, statsmodels, scikit-learn, PyMC, or similar) and SQL

  • Solid grounding in statistical inference, regression, and causal methods

  • Ability to communicate technical results to non-technical audiences — advertisers, sales, leadership

  • Excellent attention to detail and intellectual honesty about model limitations

Preferred

  • Experience in adtech, digital advertising, or media measurement

  • Experience with Bayesian methods or Bayesian MMM frameworks (e.g., PyMC-Marketing, LightweightMMM, Robyn)

  • Experience working with large-scale ad event data (impressions, clicks, conversions) and modern data stacks (e.g., Iceberg, Snowflake, BigQuery)

  • Experience in a startup or high-growth company

  • Comfort using AI tools to accelerate exploratory analysis, code, and write-ups while maintaining methodological rigor

What We Offer

  • Fully remote work environment

  • Competitive salary and equity opportunities

  • Performance bonuses

  • Health, dental, and vision benefits

  • Other benefits such as daily lunch stipend, monthly wifi, cell phone and subscription reimbursement, and annual hardware stipend

  • Flexible PTO and remote-friendly culture

  • Bi-annual Corporate Offsites

  • Opportunity to help shape a function at a rapidly scaling tech company

Apply