Greedy Questions at Bloomberg: What to Expect

Greedy algorithms are a practical and frequently tested category in Bloomberg interviews because they model real-world financial optimization problems. With 110 greedy questions in their question bank (nearly 10% of their total), Bloomberg clearly values candidates who can recognize when a locally optimal choice leads to a globally optimal solution—essential for tasks like scheduling trades, allocating resources, or minimizing latency in data feeds. Mastering these problems demonstrates you can write efficient, intuitive code under time pressure, a daily requirement on their engineering teams.

What to Expect — Types of Problems

Bloomberg’s greedy questions often fall into predictable patterns. You’ll frequently encounter interval scheduling problems, such as meeting room allocation or merging overlapping intervals, which directly relate to managing real-time data events. Array-based greedy problems, like maximizing profit from stock trades or partitioning arrays, are common due to their financial data applications. String manipulation tasks, including reorganizing strings or minimum deletions for unique characters, test your ability to handle streaming text data. Expect problems that feel practical; they often mirror internal systems for order routing, data compression, or time-series processing.

How to Prepare — Study Tips with One Code Example

To prepare effectively, focus on pattern recognition, not memorization. Start by learning the hallmarks of greedy problems: a problem asks for a maximum/minimum result, and you can construct a solution step-by-step making the best immediate choice. Always verify if a greedy approach is valid—sometimes you’ll need dynamic programming. Practice by classifying each problem into a core pattern (e.g., sorting intervals, using a priority queue for selection). Then, implement it cleanly with edge cases in mind.

A key pattern is the "jump game" style, where you determine if you can reach the end of an array given maximum jump lengths from each position. The greedy insight is to track the farthest reachable index.

def canJump(nums):
    farthest = 0
    for i, jump in enumerate(nums):
        if i > farthest:
            return False
        farthest = max(farthest, i + jump)
        if farthest >= len(nums) - 1:
            return True
    return farthest >= len(nums) - 1

Recommended Practice Order

Build competency progressively. Begin with foundational problems like "Maximum Subarray" or "Assign Cookies" to internalize the greedy mindset. Move to classic interval problems ("Merge Intervals", "Non-overlapping Intervals"). Then tackle Bloomberg-specific favorites such as "Task Scheduler" and "Minimum Domino Rotations For Equal Row". Finally, practice advanced problems like "Candy" or "Gas Station" that require subtle reasoning. Always time yourself to simulate interview pressure.

Practice Greedy at Bloomberg