The Principal Data Scientist role at Crate and Barrel is a fully remote Individual Contributor position focused on building and deploying advanced analytics capabilities. The role combines technical leadership with hands-on development to deliver production-ready systems, guide analytics strategy, and mentor data science teams.
Responsibilities
- Set the strategic direction for advanced analytics across the organization, delivering critical insights, actionable recommendations, and production-ready systems with substantial impact on key business outcomes and strategic initiatives.
- Lead the development of highly complex analytical solutions for important business challenges, influencing cross-functional strategy while ensuring solutions are scalable, reliable, and maintainable.
- Serve as a subject matter expert by providing deep technical guidance and consultation to multiple data scientist teams and senior leadership.
- Provide technical leadership and mentorship to data scientists and technical leads, supporting a culture of technical excellence, continuous learning, and knowledge sharing, and establishing and championing best practices.
- Drive innovation by identifying and integrating cutting-edge technologies and methodologies to solve complex problems and create new opportunities.
- Influence the organization’s technology direction by aligning analytics initiatives with business objectives, identifying ways to leverage data science for strategic advantage, and driving long-term value.
- Lead analysis and resolution of highly complex and unique data science issues by evaluating variable factors and creating novel approaches for expert guidance to stakeholders, including senior executives.
- Use independent judgment to select and apply advanced methods and techniques, pushing the boundaries of data science and positioning the organization as a leader in the field.
- Collaborate with executive leadership, product management, and other stakeholders to influence product strategy and translate business needs into scalable technical solutions.
- Proactively identify and mitigate major technical risks and ambiguities across projects and systems.
Requirements
- Strong capability in creating compelling, informative data visualizations for executive audiences, communicating complex information clearly and concisely to support strategic decisions.
- Extensive knowledge of data modeling techniques, including deep learning, Bayesian networks, and causal inference, with the ability to select appropriate methods for business problems.
- Extensive knowledge of statistical methods, including Bayesian analysis, causal inference, and time series analysis, with experience designing and interpreting experiments to validate findings.
- Advanced proficiency in multiple programming languages such as Python and R, along with relevant libraries including scikit-learn, TensorFlow, PyTorch, and Spark, including ability to develop and optimize complex code for performance and scalability.
- Extensive experience with big data technologies such as Hadoop, Spark, and Kafka, including designing and implementing scalable data pipelines and workflows.
- Extensive experience with cloud-based analytics platforms such as AWS, Azure, and GCP for scalable data storage, processing, model deployment, and monitoring.
- Exceptional communication, influence, and leadership skills, with the ability to explain complex technical concepts to diverse audiences.
- Recognized thought leadership in advanced analytics, including mentoring other data scientists, establishing best practices, and representing the organization in external forums and publications.
- 12+ years of progressive experience in data science, machine learning, or optimization models, including a track record of leading high-impact, company-wide or industry-shaping technical initiatives.
- Master’s degree in a quantitative field such as Mathematics, Statistics, Computer Science, Machine Learning, Operations Research, or a related field (or equivalent extensive practical experience).
- PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, or a related field is preferred.
Technologies
Python, R, scikit-learn, TensorFlow, PyTorch, Spark, Hadoop, Kafka, AWS, Azure, GCP, deep learning, Bayesian networks, causal inference, Bayesian analysis, time series analysis
Location
Remote (remote)
Compensation
USD 155,000 - 195,000 per year
Benefits
- Medical/Dental/Vision
- Life insurance and Disability
- Retirement and 401(k) match
- Paid time off, wellness time and volunteer time
- Merchandise discount and EAP resources
- Tuition Reimbursement
