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What to expect in a Bloomberg interview

How Bloomberg interviews Software Engineer and Data Scientist: 12 rounds across 2 loops, what each one tests, and how to prepare.

Practice a Bloomberg round
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Updated 4 min read

How Bloomberg interviews

These are the Bloomberg loops we have researched. Each one lists the rounds, what they test, the follow-up questions to expect, and how to prepare, with the sources we used.

Software Engineer
Mid-level · 6 rounds · 4 h
Data Scientist
Mid-level · 6 rounds · 4 h

What Bloomberg says about its interviews

Bloomberg states that its experienced engineering interview process is customized to the role. Bloomberg says that no two interview processes will be exactly alike. Engineering: Experienced Hire Application Process

Bloomberg lists application, screening, assessment, interviews, and offer as general hiring stages. Bloomberg says the process can differ by job function and experience level. How We Hire | Bloomberg LP

Bloomberg typically schedules two phone interviews for Data Science candidates before an in-house visit. Data Science: Application Process

Bloomberg Software Engineer interview

Bloomberg’s own guidance describes one or two phone interviews, including a 45- to 60-minute technical call on a computer, followed by in-house interviews. One 2025 Software Engineer candidate describes four virtual rounds of about 35 minutes, including coding and distributed-system design. The number of rounds, their order, and how much is behavioral aren’t confirmed for mid-level roles.

The 6 rounds

40 min

Interest and Role Screen

Assess motivation for Bloomberg, role fit, relevant skills, and clear discussion of prior work.

Expect: Motivation for Bloomberg and this role. Strengths, weaknesses, and relevant experience. Situation-action-outcome stories about collaboration or challenges.

Follow-ups: “Probe strengths, weaknesses, and relevant skills.” “Ask for decisions and outcomes in one past example.”

60 min

Technical Phone Coding

Assess coding fluency, problem-solving structure, and communication during a live technical screen.

Expect: Medium array or hash-map problem. Stack or string processing variation. Resume context before a coding task.

Follow-ups: “Change input size or ordering constraints.” “Ask for an optimization and its cost.” “Probe empty, duplicate, and boundary inputs.”

35 min

Onsite Coding: Stream Aggregation

Test data-structure selection, incremental processing, and ability to extend a correct solution under time pressure.

Expect: Streaming top-K aggregation. Batch totals versus real-time results. Heap or ordered-map update strategy.

Follow-ups: “Add real-time results without rescanning all products.” “Probe ties, late events, and changing K.”

35 min

Onsite Coding: Cascading Arrays

Assess stack or array reasoning, repeated transformations, and correctness on cascading edge cases.

Expect: Adjacent-run removal. Generalized run threshold. Cascades after neighboring groups merge.

Follow-ups: “Change the minimum run length.” “Add several adjacent cascades.”

35 min

Onsite System Design

Assess production-oriented design reasoning for a high-volume, filtered market-data distribution service.

Expect: Market-data fanout to many subscribers. Symbol and event-type filters. Distributed delivery, backpressure, and failure recovery.

Follow-ups: “Increase subscriber count and event rate.” “Probe slow consumers and disconnected clients.”

35 min

Onsite Behavioral and Project

Assess ownership, collaboration, impact, judgment, and reflection through specific engineering examples.

Expect: Significant project deep dive. Collaboration or conflict example. Failure, learning, and changed behavior. Motivation and job priorities.

Follow-ups: “Ask why a key decision was made.” “Probe disagreement and personal influence.”

What candidates and sources report

  • Bloomberg states that its experienced engineering interview process is customized to the role. Bloomberg says that no two interview processes will be exactly alike. Bloomberg
  • Bloomberg states that the typical experienced engineering process starts with one or two phone interviews and then continues with in-house interviews. Bloomberg
  • Bloomberg states that the first experienced engineering phone call is with Human Resources or an engineer and focuses on the candidate's interests. Bloomberg
  • Bloomberg states that a later technical phone call is with an engineer, lasts 45 to 60 minutes, and requires computer access in a quiet place. Bloomberg
  • Bloomberg describes its technical interviews as interactive and collaborative. It assesses coding fluency and problem-solving. Bloomberg

How to prepare

  • Design market-data fanout: Practice requirements, interfaces, filtering, backpressure, recovery, and scale trade-offs for many subscribers.
  • Prepare evidence stories: Build concise situation-action-outcome stories for motivation, collaboration, leadership, challenge, failure, impact, and learning.

Mistakes to avoid

  • Do not give generic reasons for wanting Bloomberg.
  • Do not describe team results without explaining your own actions.
  • Do not code before confirming input and output assumptions.
  • Do not rely on running code; inspect it and dry-run it manually.
  • Do not hide ranking costs inside vague helper functions.

Questions to ask your interviewers

  • Is the current mid-level loop still four virtual rounds of about 35 minutes?
  • Does this team require system design for the Software Engineer role?
  • How does the technical phone screen differ from the in-house coding rounds?
Practice the Software Engineer loop

Bloomberg Data Scientist interview

Bloomberg’s own materials describe a Talent Acquisition screen, two Data Science phone interviews, and three technical rounds during a later in-house visit. Phone topics include algorithms, problem solving, machine learning, natural language processing, and your experience. The exact order, lengths, tools, and how topics split across the in-house rounds aren’t published.

The 6 rounds

40 min

Talent Acquisition Screen

Confirm clear communication, relevant experience, and readiness for the Data Science process.

Expect: Concise experience and role-interest overview. Collaboration or challenge example. Process readiness and logistics discussion.

Follow-ups: “Ask what changed because of your actions.” “Probe one collaboration difficulty and its resolution.”

40 min

Phone: Algorithms and Foundations

Assess problem solving across data structures, algorithms, and foundational technical reasoning.

Expect: Data-structure selection for a constrained task. Algorithm design with complexity analysis. Foundational machine learning or NLP concept.

Follow-ups: “Change one constraint and explain how the approach changes.” “Compare your approach with one simpler alternative.”

40 min

Phone: Applied ML and Experience

Assess depth of research or project experience and the quality of applied machine learning decisions.

Expect: Research or project walkthrough. Model choice and evaluation tradeoff. Open-ended applied machine learning discussion.

Follow-ups: “Probe how you measured success.” “Ask what you would change with more time or data.”

40 min

In-House: Technical Foundations

Use one technical round to test depth across algorithmic, machine learning, and problem-solving foundations.

Expect: Algorithm or data-structure transfer. Foundational NLP or ML comparison. Failure mode and edge-case analysis.

Follow-ups: “Introduce a new data constraint and retest the approach.” “Ask for a counterexample to the initial claim.”

40 min

In-House: Applied ML Case

Assess how the candidate frames an applied machine learning or data diagnostic problem before selecting a solution.

Expect: User-journey drop-off diagnosis. Messy event data and metric design. Model proposal with operational constraints.

Follow-ups: “Change the success metric and revisit the design.” “Ask how you would diagnose a disappointing result.”

40 min

In-House: Research and Project Depth

Assess ownership, technical depth, collaboration, and learning through a substantial research or project example.

Expect: End-to-end research project review. Experiment design and result interpretation. Collaboration, challenge, and reflection.

Follow-ups: “Probe one technical decision to its evidence.” “Ask how collaboration changed the final result.”

What candidates and sources report

  • Bloomberg lists application, screening, assessment, interviews, and offer as general hiring stages. Bloomberg says the process can differ by job function and experience level. Bloomberg
  • Bloomberg says its general screening stage uses a video or telephone interview with its in-house Talent Acquisition team. Bloomberg
  • Bloomberg typically schedules two phone interviews for Data Science candidates before an in-house visit. Bloomberg
  • Bloomberg says Data Science phone interviews can cover data structures, algorithms, problem solving, foundational NLP and ML, applied ML, and open-ended ML questions. Bloomberg
  • Bloomberg says Data Science interviews often begin with a discussion of the candidate's research and experience. Bloomberg

How to prepare

  • Practice algorithm reasoning: Solve representative data-structure problems in code, state complexity, and complete a verbal manual dry-run without compiler, tests, or CI.
  • Refresh applied ML foundations: Review model choice, evaluation, leakage, NLP basics, data quality, and open-ended tradeoffs. Explain each topic with a small example.
  • Prepare project evidence stories: Build concise situation, action, outcome, and reflection stories for research, collaboration, challenge recovery, innovation, and measurable impact.

Mistakes to avoid

  • Do not give a broad résumé summary without evidence.
  • Do not claim team outcomes as your individual contribution.
  • Do not jump to an algorithm before clarifying constraints.
  • Do not state complexity without linking it to the chosen structure.
  • Do not describe a model without its evaluation design.

Questions to ask your interviewers

  • How are the two Data Science phone interviews divided across algorithms, machine learning, and experience?
  • What format and duration does each of the three in-house technical rounds use?
  • Does the current process include live coding, SQL, a case study, or a presentation?
Practice the Data Scientist loop

Sources

Research reviewed . Practice exercises are our own; they are not confidential interview questions.

Independent preparation using Bloomberg as a practice target. Not affiliated with, endorsed by, or sponsored by Bloomberg.

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