Senior Data Analyst - Supply

As a Senior Data Analyst - Supply at Dandy, you'll be tasked with developing insights that drive significant impacts within our Manufacturing, Supply Chain, and Operations teams. This involves defining key performance indicators, facilitating ad hoc decision-making, and crafting user-friendly data explorations. You'll utilize your analytical abilities to reduce ambiguity and solve complex problems within our dental operations.

To be considered for the Senior Data Analyst - Supply role at Dandy, candidates should have a minimum of 4 years of experience in operations analytics or data science, particularly in a B2B context. A bachelor's degree in a quantitative field such as statistics or computer science is required, alongside excellent skills in SQL and Python scripting.

The Senior Data Analyst - Supply plays a vital role in Dandy's operational goals by providing data-driven insights that inform critical decision-making processes. By leveraging analytics, this role directly contributes to improving efficiency in our supply chain and manufacturing operations, ensuring that we can meet the evolving needs of dentists and patients alike.

Successful candidates for the Senior Data Analyst - Supply position at Dandy should possess strong SQL and Python scripting skills and have a deep understanding of operations research and supply chain management. Furthermore, critical thinking, problem-solving abilities, and excellent communication skills are crucial to effectively translate complex data findings into actionable recommendations.

At Dandy, the work environment is fast-paced and collaborative, designed to empower innovation. As a Senior Data Analyst - Supply, you'll engage with diverse teams and have the autonomy to drive impactful analytics. We encourage a culture of learning and are committed to supporting analysts in their professional growth within our inclusive organization.

When answering this question, focus on specific projects where you've used SQL for complex data queries, how you've optimized database interactions, and the impact your analyses had on business decisions. Highlight your SQL proficiency with different databases and tools you’ve worked with.

Share a structured approach you follow for data-driven problem-solving, such as defining the problem, gathering data, analyzing results, and presenting actionable insights. Illustrate with examples from previous experiences where your method led to significant improvements or solutions.

Discuss specific examples of how you have applied analytics to supply chain challenges. Detail methodologies, tools used, and the outcomes achieved, emphasizing your contributions to improving efficiencies within the supply chain.

Explain your strategies for translating technical data insights into accessible language. This could include using visualizations, storytelling techniques, or focusing on how data impacts business objectives, ensuring that stakeholders understand and can act on your findings.

Outline the predictive modeling techniques you’re familiar with, such as regression analysis, machine learning algorithms, or time series analysis. Provide examples demonstrating how these techniques have been utilized to solve business problems effectively.

Discuss your mentoring experience by explaining the context, the specific guidance you provided, and how it impacted the colleague’s development or the project’s success. Highlight the importance of knowledge sharing in fostering a collaborative work culture.

Share your prioritization strategies, emphasizing decision-making criteria such as urgency, business impact, resource availability, and collaboration with stakeholders. Mention any tools or methods you use to stay organized and manage deadlines effectively.

Discuss the tools you find most effective for data visualization, such as Tableau, Looker, or Power BI. Provide examples of how you've used these tools to present data insights effectively and drive decisions.

Explain your processes for data validation, cleaning, and verification. Share examples illustrating how you maintained data integrity in your analyses, and the importance of accuracy in delivering reliable insights to stakeholders.

Discuss your commitment to continuous learning through resources like online courses, industry webinars, publications, or networking with professionals. Emphasize the significance of staying current to leverage new tools and methodologies in your analytics work.