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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I was approached by an outsourced recuiter via LinkedIn. After a call with him, I had an interview with the team lead and an HR rep, with whome we discussed my previous expriences, my aspirations and my motivation for working at the company. I was also presented with the main function and deliverables of the team, its structure and the goals of the role. After that, I was given a Kagle challenge to solve in Python, related to the business domain (mobility and transport). The challenge provided a dataset and required problem scoping, data exploration and manipulation, and choices for the ML approaches and models that would provide a solution. I presented my work to a panel of two team-member where I walked them through it, and I was asked about choices that I made. This interview was fairly easy. In the last round I mt members of the leadership team, in person, where we discussed my background and the company's direction and how the team fit into that. Also fairly easy, felt more like a discussion.
Questions asked: Direct copy of take home assignment:
The assignment is about a reservation system. The goal is to predict the expected revenue of a departure (flight+departure date) over time (based on the anticipation). Note Please show us what you are capable of. Make sure you are proud of the code you implement for this assignment. Architectural choices are more important than implementing all the features. Quality is key. Make sure each part of your code is behaving correctly and not susceptible to regression issues.
Some other requirements: ● The language of the assignment is python (python2 and python3 are both accepted). ● All work should be uploaded to github repository in a jupyter notebook. The outputs of the cells should be included in the jupyter notebook. Note Any python library found suitable for the assignment can be used. What is expected? ● The thought process of how the dataset is analyzed, cleaned up, and prepared for the predicting phase, is demonstrated with explanations. ● Choice of models and the prediction phase is demonstrated. Scenarios Dataset link: https://www.kaggle.com/datasets/mohammadkaiftahir/airline-dataset/data With this dataset we would like you to: ● Predict the cumulative revenue of the flight PG0708 departing on the 15th of September 2017 for each date up to 30 days before departure ● Present the features that are the most relevant to the prediction
Questions:
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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.