Siemens Data Scientist interviews reported by candidates range from a single phone call with a department head to a multi-stage process with a take-home case. Expect deep project questions and ML fundamentals.
$120K
Avg. Base Comp
$135K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe Siemens Data Scientist interviews differently, so expect variation by team. The three reports for this role are all different. One candidate who received an offer went through a recruiter call, a conversation with a team lead and HR, a business-domain take-home case on mobility and transport, a presentation to two team members, and a discussion with leadership. They described the presentation and the leadership round as fairly easy. The take-home asked for problem scoping, data exploration, model choice and a forecast of cumulative revenue ahead of departure. It also asked for clean, regression-safe code, and the interviewers asked why that candidate picked a particular model.
A second candidate, who did not get an offer, reported a baseline coding test followed by a project-focused technical interview. The questions covered transformers versus autoregressive models for time-series forecasting, how transformers handle inputs and outputs, regression types, and the physical factors behind electricity prices. A third candidate, also without an offer, had a 60-minute phone interview with a department head that walked through the resume project by project, followed by a cancelled position.
The common thread is that you should be able to defend your own projects in detail. Practice explaining data choices, model trade-offs and what you would change. Review forecasting and time-series methods, and prepare a short, honest answer for motivation and location.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Siemens process.
Going into Siemens, I knew the process would be structured but I wasn't quite prepared for how much they'd dive into my background. The first round was a coding test—nothing too intense, more of a baseline check to make sure I could write clean, logical code. After that came the technical interview, which was really project-focused. They'd read my resume carefully and asked detailed questions about specific work I'd done, walking me through my approach and the decisions I made. One thing that stood out was they asked me to compare transformers to autoregressive models for time-series forecasting, which felt like they were genuinely interested in understanding my depth in ML, not just surface-level knowledge.
What I found most valuable was that the interviewers were genuinely supportive and created space for me to think through problems. They asked about fundamental ML concepts—regression types, how transformers handle inputs and outputs, even domain-specific stuff like the physical factors that influence electricity prices, which was clearly relevant to some of their real work. The behavioral side came up too, with questions about where I saw myself in five years and logistics around location and relocation. By the end, I felt like I'd had a thorough conversation about both my technical foundation and my fit for the role, even though the outcome wasn't an offer. It was clear they were screening carefully for both capability and genuine interest in the domain problems they solve.
Prep tip from this candidate
Be ready to defend your choice of models—they specifically asked why transformers over AR models and about transformer mechanics. Also, come prepared to explain your past projects in granular detail, because they'll ask follow-up questions on your specific decisions and trade-offs.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Siemens
Given an integer N, write a function that returns all of the prime numbers up to N
| Question | |
|---|---|
| Size of Joins | |
| The Brackets Problem | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Feature Store Integration | |
| Multi-Reaction | |
| RAG Strict Source Control | |
| Training Instability in Neural Networks | |
| SARIMA in Retail Forecasting | |
| Overfit Avoidance | |
| The Pirate’s Hunt | |
| Tableau Filters and Parameters | |
| Safe Deployments | |
| Fixed-Length Arrays: Deletion | |
| Text Editor With OOP | |
| Client Solution Pushback | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| 2nd Highest Salary | |
| Merge Sorted Lists | |
| Bagging vs Boosting | |
| Bank Fraud Model | |
| Booking Regression | |
| Instagram TV Success | |
| Level Of Rain Water In 2D Terrain | |
| Covariance vs Correlation | |
| Random Forest Explanation | |
| Cyclic Detection | |
| Longest Increasing Subsequence |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report different entry points. One was approached by an outsourced recruiter on LinkedIn and had a call first. Another reported a baseline coding test that checked for clean, logical code. A third came through an employee referral. Which screen you get may depend on the team.
One candidate met a team lead and an HR representative to discuss experience, aspirations, motivation and the team's deliverables. Another had a 60-minute phone interview with a department head that went through the resume station by station and asked for implementation details. Prepare to explain each project's decisions and trade-offs.
One candidate received a Kaggle-style Python case on mobility and transport. It asked for scoping, data exploration, model choice and a revenue forecast, and the interviewers asked why that model was chosen. Another reported a project-focused technical interview that covered transformers versus autoregressive models, regression types and electricity price drivers.
The candidate who received an offer presented the take-home to two team members and answered questions about their choices. They then met members of the leadership team in person to discuss their background and the team's direction. Both were described as fairly easy and conversational. Other candidates did not report this stage, so it may vary.
