Data Scientist (f/m/x)

As a Data Scientist at ZEAL Network SE, your key responsibilities include supporting marketing with customer segmentation, consulting with LOTTO24 on data-driven business decisions, and driving the testing and analysis processes. You will design and develop models using SQL, R, and Python while defining metrics and creating dashboards to report insights effectively.

To qualify for the Data Scientist role at ZEAL Network SE, you should have experience in Data Analysis, especially with SQL and A/B testing. Proficiency in either Python or R for complex analyses is essential, alongside a strong interest in statistics. Good communication skills in English and the ability to understand business needs will also help you succeed.

ZEAL Network SE provides a modern tech environment for Data Scientists, involving tools like SQL, R, and Python to conduct analyses, design experiments, and develop metrics. You can familiarize yourself with our state-of-the-art technologies outlined in our Tech Radar, showcasing our commitment to innovation in the lottery industry.

At ZEAL Network SE, Data Scientists have access to a yearly development budget of €1,500 for personal development, encouraging ongoing learning and professional growth. You’ll work alongside talented peers in an agile environment that supports innovation and career advancement.

The culture at ZEAL Network SE is characterized by teamwork, empowerment, and ambition. Data Scientists work in a collaborative and supportive environment, free from micromanagement, and are encouraged to contribute their ideas and suggestions. With regular team events and a focus on work-life balance, it's a dynamic place to grow your career.

A good approach would begin by identifying the business objectives and understanding customer behavior. I would collect relevant data, using SQL to extract insights, perform exploratory data analysis in Python or R to identify patterns, and then apply clustering algorithms for segmentation. Lastly, I would present my findings, ensuring they align with business goals to drive decisions.

I have hands-on experience designing A/B tests to evaluate the impact of changes in product features or marketing strategies. I ensure proper control groups are established, define clear metrics for success, and analyze the results using statistical methods, guaranteeing that the conclusions drawn are data-driven and actionable.

Prioritizing tasks begins with effective communication with stakeholders to understand their needs and urgency levels. I would assess the potential impact of each request and align them with overall business objectives. Utilizing project management tools helps streamline my workflow, ensuring that critical tasks are addressed promptly while deadlines are met.

I am proficient in Python and R for data analysis, using SQL for database management. I also have experience in using tools for data visualization and dashboard creation, enabling stakeholders to access insights effectively. My ability to quickly learn new tools as needed further enhances my adaptability.

Handling data quality issues begins with thorough data validation, where I identify inconsistencies and missing values. I employ techniques like imputation for missing data and outlier detection to ensure the results are reliable. Regular communication with data providers helps maintain data integrity in ongoing projects.

In a previous project, I conducted customer behavior analysis that uncovered critical insights on user preferences. By implementing targeted marketing strategies based on these findings, my team increased user engagement by 30%. This experience reinforced the importance of data-driven decision-making and collaboration with cross-functional teams.

I am comfortable using a variety of statistical methods, including regression analysis and hypothesis testing. Additionally, I apply techniques such as time series analysis and statistical significance tests to ensure my analyses yield meaningful and actionable results that benefit the business.

I follow a structured approach when designing experiments, starting with defining clear hypotheses and objectives. I then develop a detailed plan outlining the methodology, sample size, and metrics for success. Throughout the experiment, I manage data collection, ensure compliance with protocols, and later analyze the results to draw informed conclusions.

Communication is crucial in my role as a Data Scientist, as it bridges the gap between technical findings and business stakeholders. I prioritize articulating complex ideas simply to ensure everyone understands the implications of the data. Regular updates and presentations of insights promote collaboration and foster data-driven decision-making.

I regularly engage with the data science community through conferences, webinars, and online courses. Subscribing to relevant journals and participating in forums keeps me informed about emerging trends and technologies in data science, ensuring that I continuously develop my skill set and apply best practices in my work.