Senior Data Analyst

As a Senior Data Analyst at Rovio, you will be responsible for collaborating with various teams to drive growth, supporting predictive models, leading data-driven projects, and driving an A/B testing culture. You will also build automated data pipelines and dashboards to enable real-time decision-making, ensuring that your insights directly influence Rovio's strategic direction.

To qualify for the Senior Data Analyst position at Rovio, strong quantitative skills are essential, particularly in statistics such as hypothesis testing and regression analysis. You should also be proficient in Python, SQL, and data visualization tools along with experience in A/B testing, ETL processes, and clear communication skills for presenting complex data effectively.

At Rovio, we prioritize work-life balance by offering a hybrid work model that allows our employees to work from the office two days a week. This flexibility fosters a collaborative atmosphere while also allowing team members to have time to focus on their personal lives.

Yes! Rovio is open to providing relocation support for candidates who are not currently based in Finland. We want to ensure that your transition is as seamless as possible so you can focus on making an impact in your new role.

The culture at Rovio is dynamic and collaborative. As a Senior Data Analyst, you'll be part of a team that values innovation and teamwork. You will have the opportunity to make meaningful contributions while enjoying a supportive work environment focused on continuous improvement.

When answering this question, aim to highlight specific projects you've worked on that involved predictive modeling. Discuss the methodologies you used, the tools you employed, such as Python or SQL, and the outcomes your models achieved. Providing quantifiable measures of success will make your response stronger.

Talk about your structured approach to A/B testing, from defining the hypothesis to setting up the tests and analyzing the data. Mention any tools you have used and how you interpret the results to guide business decisions effectively.

Discuss the data visualization tools like Matplotlib or Seaborn you are familiar with and explain why you prefer them based on their capabilities or your personal experience. Highlight how effective data visualization enhanced the insights derived from your analyses.

Provide a specific example where you had to present data insights to non-technical stakeholders. Focus on how you simplified the technical aspects into understandable concepts and how you engaged your audience with impactful visuals or storytelling.

Choose a situation where you identified a data quality issue or misalignment during a project. Explain the steps you took to investigate and resolve the problem, including collaboration with other teams, and emphasize the positive outcome.

Discuss your methodology for data verification, including validation techniques, cross-verification with other data sources, and peer reviews. Emphasize your commitment to high-quality analysis to support business decisions.

Mention specific resources such as blogs, online courses, or professional communities you engage with to remain current. This shows your commitment to ongoing learning and adapting to new tools and methodologies.

Detail your level of proficiency with SQL, discussing specific functions you've used for data manipulation, query optimization techniques, and experiences with complex database structures to showcase your expertise.

Explain your time management strategy, focusing on how you assess project urgency and importance. Discuss any tools or frameworks you use to keep track of your progress and ensure all projects are delivered on time.

Provide insights into your experience with machine learning, highlighting specific algorithms you've worked with. Describe how you applied these techniques to real-world problems and what the outcomes were, demonstrating your hands-on experience.