Citi·Data Scientist·Technical Phone Screen
Jul 2026
Quant-heavy technical screen for a Data Scientist role at Citibank. The whole thing was binomial tree pricing, both equity options and fixed income, which I wasn't expecting to be this deep for a DS position. Left feeling like I'd done okay on the mechanics but probably fumbled the conceptual explanation on the American call question.
- Given a two-period binomial stock model with S0=100, up factor 1.25, down factor 0.8, and a 5% per-period risk-free rate, compute the risk-neutral probability and price a European call with strike 100 using backward induction.
- Using the same binomial tree, price an American put with strike 100 and identify any nodes where early exercise is optimal.
- Prove whether early exercise of an American call on a non-dividend-paying stock can ever be optimal in this binomial framework, and explain the intuition.
- Using a two-period short-rate tree with given up and down rates and risk-neutral probability of 0.5, price a zero-coupon bond maturing at t=2 with face value 100.
- Price a two-period coupon bond with face 100 and coupon 5 paid at t=1 and t=2 using the same short-rate tree, then report its yield to maturity and explain the no-arbitrage pricing principle behind your approach.
“The risk-neutral probability calculation itself is fine, just plug into the formula.” The rest of the author's notes on Data Scientist interview at Citi, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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