OneMain Financial·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Case-style interview for a Data Scientist role at OneMain Financial, built around running a mock software company through a profit optimization exercise. Pretty quantitative for a DS screen, felt more like a micro econ exam than anything else.

Questions Asked (1)

You're given a software company's fixed costs, variable costs, and a price-demand curve. Find the profit-maximizing price, and tell me what additional data you'd want before standing behind your recommendation.

Pricing & MonetizationProduct Analytics & MetricsAdaptability & Ambiguity

Author's notes

I knew the marginal revenue equals marginal cost rule going in, but actually working through it with made-up numbers on the fly was messier than expected.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining the profit function as (Price - Variable Cost) * Quantity - Fixed Costs, using the price-demand curve to express quantity as a function of price. Then find the price that maximizes profit by taking the derivative and setting it to zero, while also checking boundary conditions and practical constraints. Finally, discuss additional data needed to validate the recommendation, such as competitor pricing, customer segmentation, and cost structure assumptions.

Pro tip: Acknowledge that the mathematical optimum is a starting point, but real-world pricing decisions require considering strategic factors like customer lifetime value, competitive response, and regulatory constraints—especially in financial services where pricing can be sensitive.

1. Define the profit function

Express profit as total revenue minus total costs, where revenue = price * quantity (from demand curve) and total costs = fixed costs + variable cost per unit * quantity.

2. Find the profit-maximizing price

Take the derivative of profit with respect to price, set it to zero, and solve for price. Also check second-order conditions and boundaries (e.g., price cannot be negative or exceed willingness to pay).

3. Validate assumptions and constraints

Consider whether the demand curve is accurate, if costs are truly fixed/variable, and if there are capacity constraints or regulatory limits that affect the optimal price.

4. Identify additional data needs

List data such as competitor prices, price elasticity by segment, customer acquisition costs, and potential market response to price changes to refine the recommendation.

5. Communicate recommendation with caveats

Present the calculated price as a baseline, but emphasize that it should be tested via A/B testing or pilot before full rollout, and adjusted based on strategic goals.

Key Points to Mention

Profit maximization occurs where marginal revenue equals marginal cost, but with fixed costs, it's where the derivative of profit w.r.t. price is zero.
The price-demand curve could be linear, log-linear, or based on historical data; its functional form affects the optimal price.
Fixed costs do not affect the profit-maximizing price in the short run, but they matter for long-run viability and break-even analysis.
Additional data: competitor pricing, cross-price elasticity, customer segmentation, willingness to pay, and potential cannibalization.
Consider non-price factors like product features, brand, and distribution that influence demand.
In financial services, regulatory compliance and fair lending practices may constrain pricing strategies.

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