Greedy Questions at Morgan Stanley: What to Expect

Greedy algorithms are a practical and frequently tested topic in Morgan Stanley’s technical interviews. With 8 out of their 53 total coding questions tagged as Greedy, it’s a pattern you cannot afford to overlook. At its core, a greedy approach builds a solution piece by piece, always choosing the next piece that offers the most immediate benefit. This makes it highly efficient and well-suited for optimization problems where local optimal choices lead to a global optimum. For a firm like Morgan Stanley, which values performance and real-time decision-making in financial systems, the ability to recognize and implement efficient greedy solutions demonstrates strong algorithmic intuition and problem-solving speed.

What to Expect — Types of Problems

Morgan Stanley’s greedy questions typically fall into a few predictable categories. You’ll often encounter interval-based problems, such as scheduling meetings or merging intervals, where you need to maximize or minimize the number of non-overlapping events. Another common type is the assignment or resource allocation problem, like assigning tasks to minimize wait time or cost. Coin change or making change with the fewest coins (when the coin system allows for a greedy solution) is also a classic. Additionally, problems involving sorting and then making a series of optimal choices—like reorganizing strings or partitioning arrays—are fair game. The key is that these problems usually involve sorting first, then iterating through the data to make a series of “best at the moment” decisions.

How to Prepare — Study Tips with One Code Example

To prepare effectively, start by solidifying your understanding of when a greedy algorithm is applicable. A problem often has greedy properties if it has an optimal substructure and the greedy choice property—meaning a local optimal choice leads to a global solution. Practice is critical: manually walk through examples to verify the greedy approach works before coding. A common strategy is to sort the input data first, as many greedy solutions rely on processing items in a specific order.

Consider the classic “Minimum Number of Arrows to Burst Balloons” problem, which is an interval scheduling variant. The goal is to find the minimum number of points (arrows) needed to burst all balloons (intervals). The greedy approach is to sort the balloons by their ending coordinate, then shoot an arrow at the end of the first balloon, and remove all balloons burst by that arrow, repeating the process.

def findMinArrowShots(points):
    if not points:
        return 0
    points.sort(key=lambda x: x[1])
    arrows = 1
    current_end = points[0][1]
    for start, end in points[1:]:
        if start > current_end:
            arrows += 1
            current_end = end
    return arrows

Recommended Practice Order

Build your competency progressively. Begin with foundational problems like “Assign Cookies” or “Lemonade Change” to understand basic greedy decision-making. Move on to interval problems, such as “Merge Intervals” and “Non-overlapping Intervals,” as they are highly relevant. Then, tackle more complex problems like “Task Scheduler” or “Gas Station,” which combine greedy logic with other concepts. Always analyze the problem to confirm a greedy approach is valid—blindly applying it can lead to incorrect solutions. Time yourself to simulate interview pressure.

Practice Greedy at Morgan Stanley