Capital One·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Capital One data scientist interview with a meaty pricing and content optimization case. Single question but it had a lot of moving parts and I don't think I covered all of them cleanly.

Questions Asked (1)

For an online content startup, how would you decide how many shows to produce and what subscription price to set? Walk through your optimization objective and constraints, the minimum data you'd need, how you'd estimate that data, and how your answer changes if variable cost per subscriber rises from $4 to $6.

Pricing & MonetizationProduct StrategyA/B Testing & Experimentation

Author's notes

This question is basically four questions stapled together and I did not pace myself well.

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AI HintsAI Generated

Suggested Approach

Frame the problem as a profit maximization over two decision variables: number of shows (content investment) and subscription price. Use a simple model where demand depends on both price and content quality/quantity, then solve for the optimal combination given cost structure and constraints. Walk through data needs, estimation methods, and sensitivity to variable cost changes.

Pro tip: Emphasize that you would validate your model with a controlled experiment (e.g., A/B test pricing and content bundles) before full rollout, and that you'd monitor elasticity and churn continuously to adapt as the market responds.

1. Define objective and constraints

Set profit = (price - variable cost) * subscribers - fixed content cost. Constraints include budget, production capacity, subscriber acquisition limits, and competitive/regulatory factors.

2. Model demand as a function of price and content

Assume subscribers = f(price, number of shows), e.g., a log-linear demand curve with price elasticity and content elasticity. Estimate these elasticities from historical data or experiments.

3. Identify and estimate required data

Need: price elasticity of demand, content elasticity (how subscribers change with shows), variable cost per subscriber, fixed cost per show, and current subscriber base. Estimate via regression on past data, conjoint analysis, or A/B tests.

4. Solve for optimal price and number of shows

Maximize profit function using calculus or numerical optimization. Check second-order conditions and sensitivity to parameter uncertainty.

5. Analyze impact of variable cost increase

When variable cost rises from $4 to $6, the optimal price increases and optimal number of shows may decrease (since margin per subscriber falls). Re-solve and compare, noting that the magnitude depends on elasticities.

Key Points to Mention

Profit maximization objective with price and content quantity as decision variables
Demand function incorporating both price elasticity and content elasticity
Data needs: historical pricing, subscriber counts, content releases, costs
Estimation methods: regression, conjoint analysis, A/B testing
Sensitivity analysis and scenario planning for cost changes
Validation through experimentation before full implementation

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.