Two Sigma OA HackerRank Interview Experience (2026) - Time Series & Graph Problems

This is a classic sweep-line problem dressed up in infrastructure language. My read on it: convert each interaction into two events, a +1 at the start and a -1 just after the end. Sort events by time, with the tiebreak that +1 events come before -1 events when the timestamps collide, because the prompt says endpoint time counts toward the interaction. Walk the events, maintain a running count, and every time the count strictly exceeds the previous maximum, record the current time as the candidate answer.

The subtle part is the tiebreak. If you put -1 before +1 at identical timestamps you will undercount by one at the exact boundary, which blows up the last two hidden tests. I also tripped on the "earliest" requirement. Because the running max only updates when you strictly exceed, the first timestamp where the max is hit is recorded naturally; updating on greater-than-or-equal breaks this.

Complexity is O(N log N) for the sort and O(N) for the sweep. I finished this one in about 25 minutes.

[[problem/665?company=32|Batch Linear Regression Coefficient]] is not the same problem, but the rolling-statistics feel is in the same Two Sigma flavor of questions they like to ask.

Problem 2: Maximum Throughput

Problem: A pipeline has N sequential processing stages. Stage i has initial throughput[i] messages per minute and a per-unit scaling cost scaling cost[i] . Scaling stage i by x units costs x scaling cost[i] and multiplies its throughput by (1 + x) . Given a total budget , maximize the pipeline throughput, which is the minimum throughput across all stages.

This is the one that ate most of my clock. My first instinct was a greedy: repeatedly pour budget into the current bottleneck stage. That works on the sample but falls apart when two stages have very different scaling costs and the cheaper-to-scale stage can leapfrog. A pure greedy on current-minimum-first can over-invest in the wrong stage when the marginal cost crosses over.

The right answer is binary search on the answer. Guess a target throughput T. For each stage, compute the minimum x such that throughput[i] (1 + x) is at least T. That gives x = ceil(T / throughput[i]) - 1 when T exceeds the initial throughput, and zero otherwise. Multiply by scaling cost[i] and sum across stages. If the total is within the budget, T is feasible; otherwise it is not. Binary search on T between the current pipeline minimum and some reasonable upper bound (I used the maximum initial throughput times (1 + budget / min scaling cost + 1) to be safe).

The mistake that cost me real time: I first bounded T poorly and blew through timeouts on the wider hidden tests. Bumping the upper bound to something conservative fixed the TLE instantly. I also spent five minutes on an off-by-one in the ceil, which you can avoid by writing x = max(0, (T + throughput[i] - 1) // throughput[i] - 1) and testing it against the sample before submitting.

I ended up passing all visible tests and, based on the submit feedback, all hidden tests as well. Time left at the end: 4 minutes, barely.

[[problem/658?company=32|Analogous Arrays Count]] has a similar "counting feasible configurations under a constraint" flavor that maps to how Two Sigma likes to frame OA questions.

[[problem/645?company=32|Maximum Independent Set in Tree]] is a different topic but shows up in their broader question pool, and the tree DP patterns come up in follow-up rounds.

[[problem/644?company=32|Huffman Encoding/Decoding]] is another one from their rotation. Worth drilling because the OA pool rotates and the heap-plus-tree building shows up.

[[problem/656?company=32|Tree Partition for Minimum Flow Difference]] is the single problem I wish I had seen before the OA, because the DFS-plus-DP framing is the same mental motion as the binary-search-on-answer pattern in Problem 2.

Result

No response yet. I did enough research afterward to believe I cleared the OA, because the next step is usually a tech screen within two to three weeks and mine is still pending. I will update if I hear back.

Tips

1. Read both problems before you write a line of code. Two Sigma often pairs a "sweep-line or interval" problem with a "binary search or DP" problem. Knowing which one is your wheelhouse and going there first is worth three minutes on the clock.
2. Binary-search-on-answer is the highest-yield pattern for their OA. If a problem asks you to maximize or minimize some value subject to a budget or capacity, check whether feasibility is monotone in the answer. It almost always is for their questions.
3. Tiebreaks in sweep-line problems are the graded part. The vanilla sweep is five lines. The graded part is what happens at equal timestamps, whether endpoints are inclusive, and whether you update on strict or non-strict improvements. Re-read the prompt and test your tiebreak against the sample before submitting.
4. Bound your binary search generously. Nothing is more painful than finishing a clean solution and losing points to TLE because your upper bound was too low and the search never terminated tightly. Pad your bounds and let the log factor absorb it.
5. HackerRank's submit signal is noisy. You will see "sample test passed" and still fail hidden tests. Build your own edge cases before submitting: empty input, single element, all ties, budget of zero.
6. Do not over-optimize before you pass the obvious version. My first instinct on Problem 2 was to try a closed-form. That ate ten minutes before I abandoned it. A clean O(N log V) binary search was always going to pass, and starting with the obvious solution would have saved me real time.