> Markdown version of [/jobs/ext/3690227-principal-data-scientist-with-ai](https://www.wearedevelopers.com/jobs/ext/3690227-principal-data-scientist-with-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Principal Data Scientist with AI

- **Company:** ARKINFOCUBES LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Data Analysis, Statistical Hypothesis Testing, Python (Programming Language), SQL Databases, Transaction Data, Uncertainty Quantification (UQ)
- **Published:** October 11, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pqpelrm23h

## About the Role

* Advanced degree (Master's or PhD) in Statistics, Economics/Econometrics, Operations Research, Applied Mathematics, or a related quantitative field - or equivalent experience.
 * 8+ years building statistical and predictive models in a commercial setting, with a substantial track record specifically in pricing and/or promotion optimization.
 * Deep expertise in: regression and econometric modeling, price elasticity estimation, demand forecasting, time-series methods (trend/seasonality), causal inference, and experimental design.
 * Hands-on experience with substitution, cannibalization, and affinity/market-basket techniques.
 * Strong command of statistical inference and the ability to quantify and communicate model confidence and uncertainty.
 * Proficiency in Python and/or R and SQL; comfort working with large-scale, real-world transactional data.
 * Deep industry experience in a relevant domain (retail, CPG/consumer goods, e-commerce, or another pricing-intensive sector) - you understand the commercial realities behind the data.
 * Proven ability to lead and direct a team and to communicate complex statistical results to non-technical business leaders.

Preferred

 * Experience with commercial pricing/revenue-management platforms or having built such capability in-house.
 * Familiarity with optimization methods (constrained/mathematical optimization) applied to pricing and promo planning.

Experience operationalizing models into production decision systems and building model-monitoring/validation practices.

## Description

We're looking for a Senior Statistical Modeler to lead the analytical direction of our pricing and promotion work. This is a hands-on technical leadership role: you'll set the modeling approach, mentor and direct a team of data scientists and analysts, and own the statistical rigor behind our pricing, promotion, and demand decisions. You'll turn large, messy commercial datasets into defensible, business-ready insight - and be able to explain the "why" behind every number to non-technical stakeholders., * Set the technical direction and standards for the team across predictive modeling, elasticity estimation, and price/promotion optimization.

 * Build and oversee price elasticity models and price optimization that translate directly into pricing recommendations and revenue/margin impact.
 * Lead promotion optimization - measuring promotional lift, efficiency, and ROI, and recommending optimal promo mechanics and depth.
 * Own substitution and cannibalization analysis to understand cross-product effects of pricing and assortment decisions.
 * Direct affinity / market-basket analysis to inform bundling, cross-sell, and promotion design.
 * Model trend and seasonality and build robust demand forecasts at the SKU and category level.
 * Apply rigorous statistical analysis and inference - hypothesis testing, confidence/uncertainty quantification, experimental and quasi-experimental design (A/B tests, causal inference) - and hold the team to that standard.
 * Drive data analysis and insight generation, turning model output into clear, actionable recommendations for commercial and executive stakeholders.
 * Mentor and review the work of junior modelers and analysts; establish reproducible, well-documented modeling practices.
 * Partner with pricing, category, finance, and product teams to embed models into decision-making.