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Probabilistic Programming Bayesian Jobs in North Carolina

Probabilistic Programming Bayesian information

What are the typical challenges faced by professionals working in Probabilistic Programming with a Bayesian focus, and how can they be addressed?

Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.

What is probabilistic programming in the context of Bayesian statistics?

Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.

What is the difference between Probabilistic Programming Bayesian vs Data Scientist?

AspectProbabilistic Programming BayesianData Scientist
Required credentialsBackground in statistics, probability, programmingStatistics, computer science, or related degree
Work environmentResearch, modeling, algorithm developmentData analysis, visualization, business insights
Industry usageAI, machine learning, research projectsBusiness, finance, tech, healthcare

Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.

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

To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.
What are popular job titles related to Probabilistic Programming Bayesian jobs in North Carolina? For Probabilistic Programming Bayesian jobs in North Carolina, the most frequently searched job titles are:
What cities in North Carolina are hiring for Probabilistic Programming Bayesian jobs? Cities in North Carolina with the most Probabilistic Programming Bayesian job openings:

Lead Data Scientist, Demand Forecasting

Scout Motors

Charlotte, NC

Other

Medical, Dental, Vision, Retirement, PTO

Posted 3 days ago

New


Job description

Role Overview

Scout Motors is building its first enterprise demand forecast - and there's no sales history to build it on. 

As a VW Group-backed EV company in the run-up to launch, Scout has reservations data, an enthusiastic and loyal community, and a product about to enter the market - but not the years of historical sales a demand model usually leans on. That's the problem this role owns: building a rigorous, probabilistic forecast from the signals that do exist, and growing it into the analytical engine of Scout's planning system as real demand data arrives. 

This is the first dedicated modeling seat in Integrated Business Planning. You'll develop the demand forecasting model and own the data behind it - then partner with IT and Product Management to productionize it into the digital IBP product Scout is building, so the forecast lives where the business actually plans and decides.

What you'll do

Become part of an iconic brand that is set to revolutionize the electric pick-up truck & rugged SUV marketplace by achieving the following:

  • Build Scout's first demand forecasting model - probabilistic rather than point-estimate, forecasting unconstrained demand at the grain the business plans on. Convert reservation backlog with explicit probabilities, phase the timing of that conversion, and layer in organic demand as its own signal.  
  • Solve the cold-start problem - stand up a credible forecast before there's sales history, using reservations, configurator and order data, market analogs, and structured priors - then systematically replace assumptions with data as the market gives it to us.  
  • Own the demand data end to end - source, define, and steward the data that feeds the forecast; build the pipelines and the feature/data layer; own data quality and the shared definitions everyone downstream depends on.  
  • Partner across the business to get the inputs and outputs right - work with the teams that own the signals feeding the forecast and the teams that consume it, so the model is grounded in real inputs and lands in a format the business can actually use. 
  • Make the forecast legible - document methodology and assumptions, quantify uncertainty honestly, and explain the model's logic clearly to non-technical stakeholders in Finance, Commercial, Production, and Procurement.  
  • Productionize with IT and Product - partner with engineering to move the model from notebooks into the IBP digital product (pipelines, deployment, monitoring, retraining) 
  • Stand up forecast measurement - back-testing, accuracy tracking, and model monitoring, so the forecast improves on a known cadence as post-launch data accumulates and the model is retrained against reality.  
  • Lay the foundation for what's next - build the data and modeling base that Scout's future AI/ML planning capabilities will extend. 

Location & Travel Expectations:

  • This role may be based out of the Scout Motors corporate headquarters in Charlotte, NC
  • This role requires 4-5 days per week in the office, with regular in-person meetings and events. 
  • Applicants should expect that the role will require the ability to convene with Scout colleagues in person and travel to participate in events on behalf of the company from time to time.
What you'll bring 

We expect all Scout employees to have integrity, curiosity, resourcefulness, and strive to exhibit a positive attitude, as well as a growth mindset. You'll be comfortable with change and flexible in a fast-paced, high-growth environment. You'll take a collaborative approach to achieve ambitious goals. Here's what else you'll bring: 

  • 12+ years of applied data science or forecasting, or equivalent experience - you've built and shipped statistical or ML models that other people depend on, not just one-off analyses.  
  • MS of PhD in a quantitative discipline (statistics, applied math, physics, operations research, economics, or similar), or equivalent applied experience 
  • A strong probabilistic and time-series modeling foundation - hierarchical, Bayesian, or other methods suited to granular, sparse, and uncertain demand. You reason in distributions, not just predictions.  
  • Fluent in Python and the modern modeling stack, strong SQL, and comfortable owning data end to end - pipelines, quality, and definitions, not just the model that sits on top.  
  • Experience forecasting with sparse, new-product, or cold-start data - analogs, priors, judgment-augmented methods - and honest about the limits of each.  
  • You've put models into production alongside engineering and product; you think about deployment, monitoring, and retraining, not just the notebook.  
  • You work fluently with AI coding tools (e.g., Claude Code, Copilot, Cursor) to build, prototype, and ship faster - and have the judgment to know where they help and where they don't. 
  • You translate technical work for non-technical audiences and can defend a forecast to a skeptical, senior, cross-functional room.  
  • A high-ownership mindset, comfortable in a fast-moving, build-it-yourself environment where the data and the priorities are still maturing 
  • Nice to have: automotive, manufacturing, or other physical-product demand; demand sensing or hierarchical forecasting; ML Ops; experience embedding models inside a digital product. 
What you'll gain

The benefits of joining Scout include the chance to build products and a company from the ground up.  This is a chance to create something new and lasting - with an iconic brand at its foundation.  In addition, Scout provides competitive compensation and benefits to support your physical, mental, and financial wellbeing. Program specifics are detailed in company policies and employee benefit guides, select highlights:

  • Competitive insurance including:
    • Medical, dental, vision and income protection plans
  • 401(k) program with:
    • An employer match and immediate vesting
  • Generous Paid Time Off including:
    • 20 days planned PTO, as accrued
    • 40 hours of unplanned PTO and 14 company or floating holidays, annually
    • Up to 16 weeks of paid parental leave for biological and adoptive parents of all genders
    • Paid leave for circumstances related to bereavement, jury duty, voting time, or military leave
Why This Role Matters 

Scout's entire planning system reconciles demand against supply and this is the seat that builds the demand side of that equation. You'll own the model and the data from version one, with the rare chance to design them right before launch rather than retrofit them under pressure after. As Integrated Business Planning scales and the planning product matures, the person who built the forecast is positioned to grow into deeper technical and team ownership.

Pay Transparency

This is a full-time, exempt position eligible to receive a base salary and to participate in an annual performance bonus program. Final salary offered will be determined based on factors including but not limited to the candidate's skills and experience. The annual performance bonus program is preset and not candidate dependent.

Initial base salary range = $160,000.00 - $192,500.00

Internal leveling code: IC7