TuringDomain experts
Senior Statistical Modeler: Pricing & Promotion Optimization
Pay not published
About this role, in Turing's words
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.
What you'll do
Key responsibilities
- 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.
Show all 10 itemsHide the other 6 in Key responsibilities
- 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.
Candidate background
Required qualifications
- 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.
Quoted from selected sections of Turing's posting. The full posting may state other conditions. Read the full posting
A closer look
More context, when you need it.
Full job descriptionThe original posting, in Turing's words
Posted by Turing, reproduced here so you can judge the role before clicking. Original posting ↗
No. of positions: 1 Remote/India, EST overlap 4 hours Immediate- 1 week availability Engagement type: Contract
Senior Statistical Modeler — Pricing & Promotion Optimization
Role summary
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.
Key responsibilities
• 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.
Required qualifications
• 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.
Working with TuringPay, getting started and work, from Turing's own pages
What the platform publishes in general. These are not extra terms confirmed for this particular role.
- Getting paid
- Paid monthly, around the 5th, straight to your bank account, or inside Deel on projects run through Deel. Its help articles disagree on whether that is the 5th calendar day or business day. help.turing.com/articles/9691407757-project-onboarding, help.turing.com/articles/6582454854-what-if-i-haven-t-rec..., help.turing.com/articles/1226770947-project-offboarding
- Before your first paid task
- A short application, with tests on some roles. Turing's review article says an interview is typically required for all roles. Onboarding covers the terms, ID verification with a driver's license or passport, and tax and payment details. help.turing.com/articles/2020987592-apply-for-jobs-at-turing, help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/9691407757-project-onboarding
- How much work
- Review takes from 48 hours to 6 weeks after you finish applying, and the client makes the final choice. No reply within 6 weeks usually means you were not selected. help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/2020987592-apply-for-jobs-at-turing
- Contract
- Depending on the project, the engagement rests on Turing's Terms of Service and a Service Agreement, on Deel, or on a partner that provides the contract. help.turing.com/articles/6582454854-what-if-i-haven-t-rec...
Read on the pages linked above on 23 Sep 2026. Platforms change these terms, so check the linked page before you rely on one.
How does the pay compare?Across Domain experts roles, not this role alone
From our own daily reads of every open listing on the platforms we follow, not from Turing's posting. How we count.
Pay across the field: Domain experts
Across 693 roles in this field with a stated hourly rate in US dollars, on 18 platforms, the middle half pays $65 to $130 an hour at the top of the range, and the median is $100.
| Platform | Roles | Top of range, per hour |
|---|---|---|
| Alignerr | 149 | $24 to $200 · median $80 |
| micro1 | 130 | $28 to $400 · median $110 |
| AfterQuery | 88 | $45 to $175 · median $100 |
| Handshake AI | 79 | $30 to $300 · median $85 |
| Mercor | 78 | $55 to $450 · median $120 |
Sources and listing historyWhere this information comes from
This listing's history
- First seen here on 25 Sep 2026, and still on Turing's list when we last read it on 13 Oct 2026.
- Posted by Turing
- First seen by AIPayList
- Last seen live
- Listing ID
- 109013
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