Senior Data Scientist

As a Senior Data Scientist at Storyblok, your main responsibilities will include leading the development and implementation of advanced statistical and machine learning models to extract insights for business decisions. You will collaborate with different teams to address challenges and create data-driven solutions, mentor junior team members and Data Champions, conduct A/B testing, and manage end-to-end data science projects to ensure they align with business goals.

To qualify for the Senior Data Scientist position at Storyblok, you should hold a Master’s degree in Data Science, Statistics, Computer Science, or a related field. Additionally, 5+ years of experience in data science or a similar role are required, alongside proficiency in programming languages such as Python or R and experience with SQL for manipulating data.

The Senior Data Scientist at Storyblok plays a crucial role in identifying high-impact opportunities that drive growth. By applying machine learning techniques to vast amounts of data, you will develop actionable insights that streamline decisions across product, finance, and marketing teams, leading to informed strategies and enhanced performance.

A Senior Data Scientist at Storyblok should be familiar with programming languages like Python or R, SQL for data manipulation, and data visualization tools such as Tableau or Power BI for effectively communicating insights. Experience with cloud platforms like AWS, GCP, or Azure, as well as data transformation tools such as DBT, is also beneficial.

At Storyblok, the culture for a Senior Data Scientist is collaborative and innovative. You’ll work alongside an international team that values fun and shared goals while contributing to a data-driven culture. Mentorship and continuous learning are encouraged, allowing you to grow alongside your peers as you tackle complex data challenges.

In your response, narrate a specific instance where you encountered significant obstacles and how you overcame them. Highlight your problem-solving skills, the methodologies you applied, and the impact of your work on the business or project involved.

Discuss your systematic approach to data preprocessing, including identifying and handling missing values, normalizing data, and selecting relevant features. Explain the significance of feature engineering in model performance and provide an example from your experience.

Mention the algorithms you are proficient in and their applications. Explain why you prefer certain algorithms for specific problems, referring to past experiences that required those techniques to deliver effective results.

Detail your experience with model evaluation metrics and validation techniques. Describe how you conduct tests, such as A/B testing, to assess the effectiveness of your models in real-world scenarios and ensure continuous improvement.

Emphasize your ability to simplify complex data findings using visualizations and clear language. Provide an example where effective communication led to valuable insights being used for strategic decision-making.

Speak to the importance of collaboration by sharing examples of projects where you worked closely with product, marketing, or finance teams. Highlight your role in facilitating communication and ensuring everyone is aligned on goals and solutions.

Discuss the methods you employ to keep your skills sharpened, like following industry leaders, attending webinars, or participating in workshops. Mention specific resources that contribute to your ongoing education in data science.

Share a narrative about your mentoring experience, focusing on how you guided a junior team member through a project. Highlight the skills you passed on and the outcomes of your mentorship on both the mentee and the project.

Explain your method for assessing project urgency and importance, citing an instance where you effectively prioritized multiple tasks to meet deadlines. Discuss the criteria you use for deciding what to tackle first.

Provide insight into current trends and challenges in the field, such as data privacy or algorithm bias. Discuss your perspective on how addressing these challenges can lead to more responsible and effective data science practices.