Data Scientist

As a Data Scientist at Curaleaf, your main responsibilities will include developing time series forecasting models, analyzing business problems, and providing data-driven insights to improve decision-making across departments. You will collaborate with various teams to ensure that your analytical solutions effectively guide our operational strategies.

To qualify for the Data Scientist role at Curaleaf, candidates should have a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field. Additionally, 3-5 years of experience in data science, particularly with time series forecasting and predictive modeling, is essential.

Data Scientists at Curaleaf utilize a range of tools to perform their analyses, including Python libraries like Prophet and TensorFlow, as well as SQL for database management. Familiarity with big data tools and cloud platforms such as Azure and Snowflake is also beneficial for handling large datasets and deploying forecasting models.

Curaleaf places a strong emphasis on career growth and offers numerous opportunities for Data Scientists to expand their skills through training programs, workshops, and mentorship. As part of a high-growth company, you’ll have the chance to take on new challenges and advance your career in an innovative environment.

The culture at Curaleaf is built on collaboration, innovation, and community involvement. Data Scientists are encouraged to share ideas, work closely with cross-functional teams, and actively participate in initiatives that benefit not just the company but also the communities we serve.

When asked about your experience with time series forecasting, it's important to highlight specific models you've used, such as ARIMA or LSTM. Discuss projects where these models successfully predicted trends, the data you worked with, and how you validated model accuracy.

In answering this, describe your analytical approach, perhaps mentioning your use of data visualization to simplify complex results or your decision-making process when facing challenging business problems. Highlight your collaborative work with stakeholders to ensure effective solutions.

Discuss your methods for staying informed, like attending workshops, webinars, reading industry publications, or participating in online communities. Mention how this ongoing education applies to your role and helps you remain innovative at Curaleaf.

Provide a real-world example where you've successfully translated complex data insights into straightforward terms for non-technical teams. Highlight the strategies used, such as storytelling, visual aids, or analogies to ensure understanding and impact.

Talk about the specific Python libraries you've used, like TensorFlow and Keras, and detail projects where you implemented machine learning frameworks to build models. Emphasize your coding proficiency and any impactful work you’ve done as a result.

Discuss your approach to testing and validating models, including performance metrics you use and any techniques you've found particularly effective for improving accuracy. Include examples of how you’ve adapted models based on feedback.

Elaborate on your experience with big data tools like Azure or Snowflake, and discuss how these platforms facilitate data storage, processing, and analysis. Mention how they have improved your workflow or the sophistication of your forecasting models.

This is your opportunity to show your passion for both data science and the cannabis sector. Discuss what excites you about Curaleaf’s mission, your interest in innovating within the industry, and how you see yourself contributing to the company's goals.

Explain your project management strategies, perhaps mentioning tools or techniques you use to prioritize tasks, manage deadlines, and ensure quality results. Discuss the importance of communication and expectations in a team environment.

Describe your communication style and how you ensure that you effectively engage team members from different departments. Highlight any successful cross-functional projects you’ve participated in and what you learned from the experience.