Manager, Data Science (Marketing)

Key Skills

Business Intelligence, Data science techniques, Data Visualization, Google BigQuery, IT, Marketing Strategy, Natural Language Processing (NLP), Optimization, Planning and organization, Project management, SQL, Tableau

Job Description

The Data Science Manager is responsible for leading a team of data scientists (individual contributors), ensuring that their efforts drive business profitability, increased efficiencies and improved customer experience. This role leads overall programs, maximizing resources, partnering with project teams and business partners, and supports problem solving efforts. Data Science Managers provide leadership, mentoring, and coaching to team members, along with supporting their development of business and technical skills . Based on the specific data science team, this role would need to be Proficient in one or more data science specializations, such as optimization, computer vision, recommendation, search or NLP.

As a Data Science Manager, you will provide executive summaries to all levels of the organization and leverage your business knowledge and network to identify opportunities to leverage data science solutions. This role is expected to collaborate with other Data Science Managers across the organization for aligning strategies and effectively managing talent. These responsibilities require effective communication skills, building relationships and partnerships, and leveraging business proficiency to solutions and recommendations.

Key Responsibilities:

• 40% People Management & Team Leadership – Provide leadership, mentorship and coaching to Data Scientists (individual contributors) and evaluate performance, execution, and contribution; Attract, retain and develop top talent to build an effective and high performing team; Support team in development of business knowledge and technical skills with clear plans and regular assessment of progress; Ensure appropriate processes and job-specific requirements are properly documented; Develop onboarding and training plan for both technical and soft skills that will ensure high level of capabilities within the team; Manage resources effectively to align with strategic initiatives and business needs; Foster collaboration with team members to develop a supportive and engaged team; Support team in the design and development of algorithms and models to use against large datasets to create business insights; Drive team’s results and celebrate accomplishments
• 25% Project Management – Prioritize and assign projects by striking a balance between a variety of factors, like funding, return on investment, innovative or challenging work, among others; Manage programs for the team to ensure consistency of execution, balance of resources and quality solutions; Collaborate with program managers and business partners to align on objectives and expectations and ensure project teams deliver against them; Serve as escalation point for any issues or difficulties with specific projects; Provide executive updates to leaders at all levels of the organization on program effectiveness, team accomplishments, summary of findings and recommended solution, and explain internal and external impacts
• 15% Strategy & Business Development – Leverage extensive business knowledge and relationships to seek out new business opportunities to leverage data science and advocate for team’s skills; Effectively develop trust and collaboration with internal customers and cross-functional teams; Ensure alignment with IT support model, collaboration, tools and governance to enable data scientists to be most effective in executing their roles; Collaborate with other data science managers across the organization to establish high level strategy and optimize talent management
• 20% Technical Exploration & Development – Seek further knowledge on key developments within data science, technical skill sets, and additional data sources; Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; Ensure best practices and vision for data analysis and model productionalization are leveraged