Data Science Engineer

As a Data Science Engineer at Cornerstone Building Brands, your primary responsibilities include creating advanced data models, developing BI dashboards that tell compelling stories, and optimizing data extraction and transformation processes. You will also work closely with various stakeholders, ensuring their data infrastructure needs are met while addressing any technical challenges that arise.

Candidates applying for the Data Science Engineer role at Cornerstone Building Brands should have a college degree in Statistics, Industrial Engineering, or a related field. Additionally, a minimum of 2 years' experience with Microsoft Power BI and advanced Excel knowledge is essential. Familiarity with R, SQL, and Python, as well as strong analytical skills, would be advantageous.

Key skills needed for success as a Data Science Engineer at Cornerstone Building Brands include advanced knowledge of BI tools such as Power BI, excellent communication and presentation skills, and strong analytical abilities to handle unstructured datasets. Additionally, proficiency in visual storytelling and experience in optimizing data-driven processes are crucial for this role.

Data Science Engineers at Cornerstone Building Brands enjoy a flexible work environment, primarily working from home with the opportunity to visit the office once a month. This balance fosters collaboration while allowing for personal work-life management, leading to a productive and inclusive atmosphere.

Data Science Engineers at Cornerstone Building Brands can expect a range of benefits starting from day one, including medical, dental, and vision coverage, paid holidays, and life insurance. They also have access to professional development opportunities, Employee Resource Groups, a free virtual English course, and an Employee Assistance Program.

When responding, highlight specific projects where you've utilized Power BI effectively, detailing the complexities of the data you worked with, and how your dashboards impacted business decisions. Use examples that showcase your ability to visualize and simplify complex data.

Discuss your time management strategies, such as using project management tools and setting key milestones. Illustrate your approach to maintaining communication with stakeholders to manage expectations and align priorities effectively.

Share specific techniques you've used to optimize data workflows, such as automating processes, improving data mapping, or leveraging ETL tools. Mention any measurable outcomes that resulted from these improvements.

When responding, discuss your understanding of executive needs and preferences. Describe how you transform raw data into actionable insights, focusing on clear visualizations and concise storytelling that resonates with decision-makers.

Use a real-world example where you faced challenges working with an unstructured dataset. Explain your analysis steps, the techniques you applied to clean and model the data, and the outcomes that were derived from your work.

Highlight your communication skills and the importance of understanding different team needs. Detail any experiences where you successfully gathered requirements from multiple stakeholders and delivered results that satisfied all parties.

Articulate the principles of effective data visualization, such as clarity, relevance, and simplicity. Discuss how you tailor visualizations for different audiences and the tools you use to create impactful designs.

Mention the tools you are proficient in, such as R, SQL, and Python, and discuss how you apply them in your projects. Illustrate with examples of how these tools have aided your data analysis process.

Describe your troubleshooting process for identifying and rectifying inaccuracies, mentioning any specific methodologies or tools you employ to ensure data integrity.

Share a specific situation that required effective communication with a non-technical audience. Talk about your approach to simplifying complex concepts and using visual aids to promote understanding.