Tredence - Monetization Analyst - Analytics (4-9 yrs)

Monetization Analyst JD

We are looking for hands-on analytics professional to support the company's Monetization team in building an elasticity library, standardizing promotion measurement, and quantifying substitution impacts across pricing and promotional levers. The role requires strong problem-solving, quantitative reasoning, and a blend of business acumen and technical depth.

For this role, we are specifically looking for a strong ML/modeling profile, as this person will be a founding member of the Pricing team and will help establish core measurement and modeling foundations (elasticity, substitution/cannibalization, and promo impact).

Required/Must-have :

1. Write efficient, production-quality SQL (advanced).

2. Perform structured problem solving and root-cause analysis; translate ambiguous questions into measurable metrics and crisp analytical plans.

3. Basic modeling / ML capability to support monetization analytics work (baseline predictive/analytical modeling orientation).

4. Understanding of elasticity concepts to support pricing, fees, and promotional incentive analysis.

5. Apply advanced experimentation and causal inference in a product setting: design tests (power analysis, primary/secondary metrics), interpret results, form hypotheses, and deep dive into drivers as needed.

6. Use Python for data processing, automation, and analytics workflows

7. Communicate clearly in fluent English (written and spoken), tailoring insights to technical and non-technical stakeholders.

Preferred /Good-to-have :

1. Elasticity modeling: Ability to build and maintain an elasticity library to quantify demand response (and supply response where applicable) to pricing, fees, and promotional incentives.

2. Substitution / cannibalization measurement: Ability to quantify and explain substitution and cannibalization effects across products, offers, and promotion types.

3. Tableau reporting: Ability to build and own complex, self-service Tableau dashboards and the supporting data pipelines to enable scalable tracking and decision-making

4. Optimization methods : Exposure to optimization/solver approaches (e.g., linear programming, constrained optimization) for monetization design.

5. LLMs for analytics : Exposure to applying LLMs for analytics acceleration, automation, insight generation, or workflow augmentation.

Preferred candidate profile :

Role : Data Science & Analytics - Other

Industry Type : IT Services & Consulting

Department : Data Science & Analytics

Employment Type : Full Time, Permanent

Role Category : Data Science & Analytics - Other