Statkraft Data Scientist Interview Questions + Guide in 2025

Statkraft is Europe's largest generator of renewable energy, committed to developing sustainable power solutions that support a transition towards a 100% renewable electricity supply.

In the role of a Data Scientist within the Quantitative Development team at Statkraft, you'll be at the forefront of driving digital transformation in the Trading & Origination division. This position involves working closely with proprietary trading teams to enhance their data capabilities and analytical insights, ultimately providing a competitive edge in the energy market. Key responsibilities include building and maintaining analytical dashboards, improving data flow and architecture, developing machine learning models, and collaborating across departments to align business needs with data solutions. Proficiency in Python, strong data engineering skills (such as experience with pandas and PostgreSQL), and familiarity with machine learning frameworks will be crucial for success in this role. Additionally, candidates should possess a proactive mindset and the ability to communicate effectively across various teams.

This guide will equip you with the insights and understanding necessary to tackle interview questions confidently and align your skills with Statkraft's mission and values.

The interview process for a Data Scientist at Statkraft is structured and involves multiple stages to assess both technical and interpersonal skills.

1. Initial Screening

The process typically begins with an initial phone screening conducted by an HR representative. This conversation focuses on your background, motivation for applying, and alignment with Statkraft's values and culture. Expect questions about your resume and relevant experiences, as well as your interest in the energy sector.

2. Technical Assessment

Following the initial screening, candidates usually participate in a technical assessment. This may involve a coding exercise or a technical task related to data engineering and visualization, often conducted via a video call. You may be asked to demonstrate your proficiency in Python and other relevant tools, as well as your understanding of machine learning concepts.

3. Case Study Presentation

In some instances, candidates are required to prepare a presentation based on a real business case relevant to the role. This presentation allows you to showcase your analytical skills, problem-solving abilities, and how you approach data-driven decision-making. Interviewers will assess your ability to communicate complex ideas clearly and effectively.

4. Behavioral Interviews

Subsequent rounds typically consist of behavioral interviews with team members and managers. These interviews focus on your past experiences, teamwork, and how you handle challenges in a collaborative environment. Expect questions that explore your ability to manage multiple tasks, work under pressure, and contribute to a team-oriented culture.

5. Final Interview

The final stage often includes a meeting with higher management or stakeholders. This round may cover strategic discussions about the role and how it fits within the broader objectives of the Quantitative Development team. You may also be asked about your long-term career goals and how they align with Statkraft's mission.

As you prepare for your interview, consider the specific skills and experiences that will be relevant to the questions you may encounter. Next, let's delve into the types of questions that candidates have faced during the interview process.

Here are some tips to help you excel in your interview.

Understand the Interview Process

The interview process at Statkraft typically involves multiple rounds, including an initial phone screening followed by technical and managerial interviews. Be prepared to discuss your resume, past experiences, and how they relate to the role. Familiarize yourself with the structure of the interviews, as this will help you manage your time and responses effectively.

Prepare for Technical Assessments

Given the emphasis on data engineering and visualization skills, brush up on your knowledge of Python, data manipulation libraries (like pandas), and visualization tools (such as Plotly and Dash). You may be asked to solve technical problems or present a case study, so practice coding challenges and be ready to explain your thought process clearly.

Showcase Your Analytical Skills

Statkraft values strong analytical skills and a commercial mindset. Be prepared to discuss how you approach problem-solving, particularly in dynamic situations like dealing with missing data or performing ad hoc analyses. Use examples from your past experiences to illustrate your analytical capabilities and how they can benefit the team.

Align with Company Values

Statkraft is committed to renewable energy and sustainability. Demonstrating your passion for these values can set you apart. Be ready to discuss why you want to work at Statkraft and how your personal values align with the company's mission. This will show that you are not only a fit for the role but also for the company culture.

Communicate Effectively

Strong communication skills are essential, especially since the role involves collaborating with various teams. Practice articulating your thoughts clearly and concisely. During the interview, listen actively and ensure you understand the questions being asked before responding. This will help you build rapport with your interviewers.

Prepare for Behavioral Questions

Expect questions that assess your fit within the team and company culture. Be ready to discuss how you handle pressure, manage multiple tasks, and work collaboratively. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing concrete examples that highlight your skills and experiences.

Be Ready to Discuss Your Projects

You may be asked to present your past projects, particularly those relevant to data science and engineering. Prepare a concise presentation that outlines the problem, your approach, the tools you used, and the outcomes. This will demonstrate your technical expertise and ability to communicate complex information effectively.

Embrace a Can-Do Attitude

Statkraft values a proactive and positive mindset. Approach the interview with confidence and enthusiasm. Show that you are eager to learn and contribute to the team. This attitude can leave a lasting impression on your interviewers and may help you stand out among other candidates.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Scientist role at Statkraft. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Statkraft. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of the energy trading landscape. Be prepared to discuss your past experiences, technical knowledge, and how you can contribute to the Quantitative Development team.

Technical Skills

1. How would you prepare data for a machine learning algorithm?

This question assesses your understanding of data preprocessing, which is crucial for effective model training.

How to Answer

Discuss the steps you would take to clean, transform, and prepare the data, including handling missing values, normalization, and feature selection.

Example

“I would start by examining the dataset for missing values and outliers. For missing values, I would consider imputation methods or removing those records if they are not significant. Next, I would normalize the data to ensure that all features contribute equally to the model. Finally, I would perform feature selection to identify the most relevant variables for the model.”

2. Can you explain the difference between supervised and unsupervised learning?

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as regression and classification tasks. In contrast, unsupervised learning deals with unlabeled data, where the model tries to identify patterns or groupings, like clustering algorithms such as K-means.”

3. Describe a machine learning project you have worked on. What challenges did you face?

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Outline the project, your role, the challenges encountered, and how you overcame them.

Example

“I worked on a project to predict energy consumption using historical data. One challenge was dealing with missing data points. I implemented a time-series analysis to fill in gaps based on trends. This improved the model's accuracy significantly.”

4. What machine learning libraries are you familiar with, and how have you used them?

This question gauges your technical proficiency with relevant tools.

How to Answer

Mention specific libraries and describe how you have utilized them in your projects.

Example

“I am well-versed in libraries such as scikit-learn for building models, pandas for data manipulation, and TensorFlow for deep learning applications. For instance, I used scikit-learn to implement a random forest classifier for a classification problem, which provided excellent results.”

5. How do you evaluate the performance of a machine learning model?

This question assesses your understanding of model evaluation metrics.

How to Answer

Discuss various metrics and when to use them, such as accuracy, precision, recall, and F1 score.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, precision and recall for imbalanced datasets, and the F1 score to balance both. I also use cross-validation to ensure the model generalizes well to unseen data.”

Data Engineering

1. What experience do you have with data visualization tools?

This question focuses on your ability to communicate data insights effectively.

How to Answer

Mention specific tools and how you have used them to visualize data.

Example

“I have experience using Plotly and Dash for creating interactive dashboards. In a previous project, I built a dashboard to visualize energy consumption trends, which helped stakeholders make informed decisions.”

2. How do you handle missing data in a dataset?

This question tests your data cleaning skills.

How to Answer

Discuss various strategies for dealing with missing data, including imputation and removal.

Example

“I handle missing data by first assessing the extent of the missingness. If it’s minimal, I might use imputation techniques like mean or median substitution. For larger gaps, I consider removing those records or using predictive modeling to estimate the missing values.”

3. Can you explain the importance of data normalization?

This question evaluates your understanding of data preprocessing techniques.

How to Answer

Discuss why normalization is necessary and the impact it has on model performance.

Example

“Normalization is crucial because it ensures that features contribute equally to the distance calculations in algorithms like K-means clustering. Without normalization, features with larger ranges can dominate the model, leading to biased results.”

4. Describe your experience with SQL and database management.

This question assesses your ability to work with databases.

How to Answer

Mention your experience with SQL queries and any database management systems you have used.

Example

“I have used PostgreSQL extensively for data extraction and manipulation. I am comfortable writing complex SQL queries to join tables, filter data, and aggregate results for analysis.”

5. How do you ensure data quality in your projects?

This question focuses on your approach to maintaining high data standards.

How to Answer

Discuss methods you use to validate and clean data.

Example

“I ensure data quality by implementing validation checks during data collection, performing regular audits, and using automated scripts to identify anomalies. This proactive approach helps maintain the integrity of the data throughout the project lifecycle.”