Data Science Manager - One Tasking

About the role

The Data Science Manager leads a team of data scientists (individual contributors) so that their work drives business profitability, greater efficiency and a better customer experience. The manager leads programs, partners with project teams and business partners, mentors and coaches team members, and provides executive summaries to all levels of the organization. The role sits in the Store Support Center and typically reports to a Senior Manager or above.

Key responsibilities

• People management: lead, mentor and coach data scientists, evaluate performance, attract and retain talent, document processes and requirements, and run onboarding and training plans for technical and soft skills.
• Project management: prioritize and assign projects, manage programs for consistent execution and quality, act as escalation point for project issues, and give executives updates on program effectiveness and findings.
• Strategy and business development: use business knowledge and relationships to find new opportunities for data science, build trust with internal customers and cross-functional teams, and collaborate with other data science managers on strategy and talent.
• Technical exploration: follow developments in data science, improve analytics by creating replicable solutions such as codified data products and process flowcharts, and promote best practices for model production.

Required profile

• At least 9 years of experience in business intelligence and analytics (preferred).
• Experience managing and growing team members in a professional setting (preferred).
• Bachelor's degree or equivalent in a related field.
• Legal permission to work in the United States; must be 18 or older.

Required skills

• Proficiency in one or more data science specializations such as optimization, computer vision, recommendation, search or NLP.
• Running queries against data, preferably with Google BigQuery or SQL.
• Data visualization software, preferably Tableau.
• Predictive modeling, data mining and data analysis.
• Statistical techniques and prescriptive modeling.