New opportunity

Senior Specialist, Strategy Pricing & Interchange

Mastercard · Gurgaon, India

About this role

Role Overview: Mastercard is seeking a highly analytical professional to join the RAISE Value Quantification team within Strategy Pricing & Interchange. This role focuses on translating product, service, and customer value into scalable pricing strategies, business cases, and decision-ready analytics. You will partner with Product, Pricing, Commercialization, and Data Science teams to build value-based pricing models and quantify customer impact. Key Responsibilities: Predictive Modeling and Explainability: Design and build predictive models to quantify the economic, operational, and customer value of Mastercard products. Translate model outputs into clear value stories and business cases. Pricing Analytics & Modeling: Develop value-based B2B pricing models, commercial frameworks, and analytical tools. Estimate adoption, revenue impact, churn elasticity, and outcome-based scenarios. Machine Learning & Advanced Analytics: Apply machine learning and statistical techniques to identify pricing opportunities and customer value drivers. Build forecasts and analytical outputs to support strategic decision-making. Qualifications: Education: Master's degree in a quantitative field (Data Science, Statistics, Economics, Engineering, Finance, Mathematics, Computer Science, or Operations Research). Experience: 6-8 years of experience in data science and pricing analytics, with a proven track record in predictive modeling and value quantification. Technical Skills: Proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and advanced SQL. Strong knowledge of forecasting, statistics, and model explainability. Business Skills: Experience with pricing elasticity, revenue optimization, and financial modeling. Professional Skills: Excellent communication and storytelling skills with the ability to influence senior stakeholders. Preferred Qualifications: Experience in payments, fintech, banking, SaaS, or consulting. Advanced Excel skills (Power Query, financial modeling) and experience with visualization or dashboarding tools.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Predictive ModelingMachine LearningPricing AnalyticsData SciencePythonPandasNumPyScikit-learnStatsmodelsSQLStatistical ModelingForecastingModel ExplainabilityFinancial ModelingRevenue OptimizationStakeholder Management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Value-Based Pricing Modeling
The core of this role is translating product and customer value into scalable B2B pricing frameworks. You must demonstrate the ability to build models that quantify economic impact, churn elasticity, and revenue outcomes to support strategic commercial decisions.
Predictive Analytics and Explainability
You are expected to design and build predictive models using Python and statistical techniques. A critical priority is the ability to explain complex model outputs to non-technical senior stakeholders, ensuring that data-driven insights are actionable and transparent.
Cross-Functional Commercial Influence
Success requires partnering with Product, Pricing, and Data Science teams to align analytical outputs with business goals. You must be able to translate technical findings into clear value stories that influence executive-level commercial strategies.

Likely challenges to think through

Balancing Technical Rigor with Commercial Utility
A common challenge in value quantification is ensuring that sophisticated machine learning models remain practical for business decision-making. Prepare by practicing how to simplify complex statistical outputs into business cases that address specific revenue or cost-saving questions.
Managing Data Complexity in Payments
The role involves analyzing large, complex datasets to identify pricing drivers. You may face challenges in data preparation and feature engineering. Prepare by reviewing your experience with SQL and Python (Pandas/NumPy) in handling high-volume, multi-dimensional financial data.
Navigating Stakeholder Expectations
Influencing senior leaders requires high-level storytelling and the ability to defend your methodology under scrutiny. Prepare by anticipating questions regarding model assumptions, sensitivity analysis, and how your pricing recommendations account for market-specific variables.

Practice your answers

Technical practice

Predictive Modeling for Value Quantification

What to coverExplain your approach to designing a predictive model that quantifies the economic value of a B2B product. Focus on how you select features, validate model performance, and ensure the output is actionable for commercial stakeholders.

Pricing Elasticity and Revenue Optimization

What to coverDescribe how you estimate churn elasticity when adjusting pricing for a service. Discuss the statistical techniques you use to isolate price impact from other market variables and how you balance revenue growth with customer retention.

Model Explainability in Commercial Settings

What to coverWhen using complex machine learning models to inform pricing, how do you ensure the results are explainable to non-technical business leaders? Suggest methods for translating model coefficients or feature importance into clear business narratives.

SQL for Complex Data Transformation

What to coverDescribe a scenario where you had to perform complex data extraction and transformation using SQL to prepare a dataset for predictive modeling. Focus on how you handled data quality issues and optimized queries for large-scale datasets.

Python Libraries for Statistical Analysis

What to coverCompare the use of Scikit-learn versus Statsmodels for pricing analytics. Explain when you would prioritize the predictive power of Scikit-learn over the statistical rigor and inference capabilities of Statsmodels.

Forecasting and Scenario Analysis

What to coverHow do you build robust forecasts for product adoption and revenue impact? Explain your process for running sensitivity analyses to test the impact of different pricing scenarios on overall business outcomes.

Value-Based Pricing Frameworks

What to coverWhat are the core components of a value-based pricing model compared to cost-plus pricing? Discuss how you quantify 'customer value' in a data-rich environment like payments or fintech.

Data Visualization for Strategic Decision-Making

What to coverHow do you leverage visualization tools to present complex analytical outputs to senior leadership? Describe the principles you follow to ensure dashboards or reports drive immediate strategic action.

Behavioural practice

Influencing Cross-Functional Stakeholders

What to coverDescribe a time you had to influence a product or commercial team to adopt a pricing strategy based on your data analysis. How did you handle resistance and ensure your recommendations were aligned with business goals?

Translating Technical Insights to Business Value

What to coverProvide an example of a complex technical project where you had to translate your findings into a 'value story' for senior management. What was the situation, what actions did you take, and what was the final business impact?

Managing Ambiguity in Analytical Projects

What to coverTell me about a time you were tasked with a pricing or modeling challenge with limited or messy data. How did you approach the problem, and what steps did you take to ensure the reliability of your final output?

Ownership and Independent Problem Solving

What to coverDescribe a situation where you identified a pricing opportunity or performance trend independently. How did you validate your hypothesis and move the initiative forward within your organization?

Sharpen your knowledge

Topics to prepare

Value-Based Pricing Principles
Review the conceptual framework of B2B value-based pricing versus cost-plus models. Exercise: Draft a hypothetical business case for a new payment service, identifying key value drivers like churn reduction and operational efficiency. This is critical for translating complex model outputs into actionable commercial strategies.
Model Explainability in Commercial Contexts
Study techniques for interpreting machine learning models (e.g., SHAP or LIME) to explain pricing decisions to non-technical stakeholders. Exercise: Prepare a summary of a predictive model's results that focuses on business impact rather than technical metrics. This aligns with the requirement to influence senior leadership.

Skills to revise

Predictive Modeling with Python
Practice building regression and classification models using Scikit-learn and Statsmodels. Exercise: Create a script to estimate price elasticity using historical transaction data. This directly addresses the core responsibility of quantifying customer impact and revenue scenarios.
Advanced SQL for Data Analysis
Focus on complex joins, window functions, and subqueries to aggregate large datasets for pricing analysis. Exercise: Write a query to calculate year-over-year revenue trends segmented by customer type. This is essential for the data-heavy nature of the RAISE Value Quantification team.
Financial Modeling in Excel
Refine skills in building dynamic financial models using Power Query and sensitivity analysis tools. Exercise: Build a scenario-based model that calculates the net present value of a product launch under different adoption rates. This is a preferred qualification for supporting strategic decision-making.

Your preparation checklist

Develop a value-based pricing case study
Prepare a portfolio piece demonstrating how you used predictive modeling to quantify the economic value of a product. Focus on your methodology for estimating churn elasticity or revenue impact, ensuring you can explain the technical model and the resulting business recommendation.
Refine your Python and SQL technical toolkit
Review your proficiency in Python libraries (Pandas, NumPy, Scikit-learn, Statsmodels) and advanced SQL. Be prepared to discuss how you have used these tools to clean, transform, and analyze large, complex datasets for commercial decision-making.
Prepare examples of model explainability
Identify instances where you had to translate complex machine learning outputs into clear, actionable insights for non-technical stakeholders. Practice articulating how you balanced statistical rigor with the need for business-ready narratives.
Review financial modeling and revenue optimization principles
Brush up on core concepts such as pricing elasticity, customer economics, and sensitivity analysis. Be ready to explain how you have applied these concepts in previous roles to support strategic trade-offs.
Formulate questions for the interviewers
Prepare thoughtful questions regarding the team's current approach to value quantification, how they collaborate with Product and Commercialization teams, and the specific types of payment data challenges they are currently prioritizing.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…