The reported Siemens Data Scientist process combined background discussions, a Python take-home forecasting challenge, a presentation defending modeling choices, and a leadership conversation.
$110K
Avg. Base Comp
$160K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
For this Siemens Data Scientist interview, the clearest preparation focus is a Python forecasting take-home built around business reasoning, followed by a discussion of the choices behind it. One candidate first spoke with a team lead and HR representative about prior experience, motivation, aspirations, the team’s deliverables, and the role’s goals. That makes it worth preparing a concise account of your work and why Siemens’ domain interests you.
The technical exercise used a mobility-and-transport reservation dataset and asked the candidate to scope the problem, explore and manipulate data, select ML approaches, and predict cumulative flight revenue ahead of departure. The candidate presented the work to two team members and was asked why a particular forecasting model type was chosen. Practice explaining the data-cleaning decisions, feature relevance, forecast target, validation approach, and model tradeoffs in a way that connects each choice to the business question. Code quality and architecture were explicitly emphasized in the assignment description, so a readable notebook and defensible structure matter alongside model results.
The reported final conversation was in person with leadership and centered on background, company direction, and how the team fit within it. Siemens’ industrial technology work gives useful context for framing data work around practical decision-making, but the format below reflects one reported experience, so timing and exact content may vary.
Synthesized from 1 candidate report by our editorial team.
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Featured question at Siemens
Given an integer N, write a function that returns all of the prime numbers up to N
| Question | |
|---|---|
| The Brackets Problem | |
| Size of Joins | |
| Hurdles In Data Projects | |
| Find Duplicate Numbers in a List | |
| Multi-Reaction | |
| Training Instability in Neural Networks | |
| Overfit Avoidance | |
| SARIMA in Retail Forecasting | |
| Tableau Filters and Parameters | |
| Safe Deployments | |
| Fixed-Length Arrays: Deletion | |
| Text Editor With OOP | |
| The Pirate’s Hunt | |
| Client Solution Pushback | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| 2nd Highest Salary | |
| Bagging vs Boosting | |
| Bank Fraud Model | |
| Booking Regression | |
| Instagram TV Success | |
| Level Of Rain Water In 2D Terrain | |
| Covariance vs Correlation | |
| Random Forest Explanation | |
| Longest Increasing Subsequence | |
| Lasso vs Ridge | |
| Assumptions of Linear Regression | |
| Out of Stock Inventory | |
| Bias vs. Variance Tradeoff |
Synthesized from candidate reports. Individual experiences may vary.
A candidate reports an initial discussion with the team lead and an HR representative covering previous experience, aspirations, motivation for joining Siemens, and the team’s function, structure, deliverables, and role goals. Prepare a focused narrative that connects your experience to the role.
The reported assignment used a reservation-system dataset and asked for cumulative revenue prediction before a flight’s departure. It required problem scoping, data exploration and manipulation, model selection, and an explanation of relevant features; the supplied brief emphasized code quality and architectural choices.
The candidate presented the completed take-home to a panel of two team members and explained the work. Candidates may be asked to defend modeling decisions, including why a particular forecasting model type was selected, so rehearse a clear rationale and the tradeoffs you accepted.
In the reported last round, the candidate met leadership in person to discuss their background, Siemens’ direction, and how the team fit within it. The candidate described this as more of a discussion than a difficult technical interview; prepare thoughtful questions about the team’s mission and impact.