Director of Data Science

Position Overview:

As Director of Data Science, you will play a crucial role in creating, leading, and managing a team of data and analytical professionals to drive impactful insights and data-driven decision-making across the organization. You will oversee the entire data lifecycle, from data acquisition and warehousing to model development, validation, and deployment. Your team will be responsible for MLOps/DataOps, platform engineering, data engineering and ETL, and data science and analytics. You will collaborate closely with cross-functional teams, including product managers, engineers, and business stakeholders, to identify opportunities, define project goals, and ensure the successful execution of data initiatives.

Key Responsibilities:

• Team Leadership and Management:
• Build a high-performing team, fostering a culture of innovation, collaboration, and continuous learning.
• Set clear objectives, provide regular feedback, and support professional growth for team members.
• Allocate resources effectively, manage workloads, and prioritize projects to meet business needs and deliver high-quality results.
• Data Architecture and Engineering:
• Own the end-to-end data and BI process, ensuring data integrity, accuracy, and security.
• Oversee the development and deployment of a cloud-first data architecture to meet business needs.
• Implement best practices to create a high-performing engineering organization using modern software engineering and DevOps principles.
• Foster a culture of continuous improvement and engineering quality throughout all data science and engineering activities.
• Design processes and systems to achieve effective data governance and management without impeding productivity.
• Data Strategy and Planning:
• Work closely with senior stakeholders to understand business objectives and define data strategies that align with organizational goals.
• Identify opportunities to leverage data science techniques to solve complex business problems and drive data-driven decision-making across the company.
• Develop and maintain a roadmap for data projects, ensuring alignment with business priorities, data governance principles, and regulatory requirements.
• Project Management:
• Collaborate with cross-functional teams to define project goals, scope, and deliverables.
• Own the prioritization of deliverables to ensure alignment with business objectives and resource availability.
• Develop a framework for managing data science and engineering work using principles and concepts from Agile software engineering.
• Drive the end-to-end data lifecycle, from data acquisition and warehousing to model development, validation, and deployment.
• Work closely with stakeholders and customers to deliver regular updates on progress, communicate challenges, and gather feedback and direction when confronting tradeoffs.
• Ensure high visibility on project timelines and budget and proactively seek input and prioritization as new information is obtained.
• Data Analysis and Modeling:
• Guide team members in identifying appropriate analytic approaches to solving business questions.
• Oversee developing and implementing advanced statistical and machine learning models to extract insights and solve complex problems.
• Provide guidance on data acquisition, preprocessing, feature engineering, and model selection techniques.
• Collaboration and Communication:
• Collaborate closely with product managers, engineers, and business stakeholders to understand and translate their requirements into data projects.
• Present findings, insights, and recommendations to senior leadership and non-technical stakeholders clearly and concisely.
• Foster strong relationships with key stakeholders, promote data-driven decision-making, and influence strategic initiatives.
• Challenge the status quo, help guide entrepreneurial innovation in analytical products, and curate new data sources, elevating analytics beyond data reporting and dashboarding to deliver actionable insights.

Qualifications:

• A bachelor's degree is required, ideally in a quantitative field such as Computer Science, Mathematics, Statistics, Physics, or related disciplines. A master's degree is preferred.
• 10+ years of relevant experience is required in data science, data engineering, or software engineering.
• 7+ years of experience in data science and engineering leadership roles (both as a people manager and as a technical lead), with at least 5 years demonstrated experience as a people manager.
• Experience with the full data lifecycle, including acquisition, warehousing, reporting, and modeling.
• Expertise in ETL/ELT, data transformation, and data quality assurance.
• Experience using cloud tools and managing a data infrastructure.
• Familiar with modern software engineering practices, including CI/CD, infrastructure-as-code, and test-driven development.
• Expertise in statistical modeling and machine learning.
• Familiarity with principles of data visualization as well as common business intelligence tools; experience with Tableau.
• Experience with data warehousing and dimensional modeling; familiarity with Kimball-Ross, Inmon, Medallion architecture, and data mesh approaches to data modeling and warehousing.
• Excellent written and verbal communication skills for coordinating across teams.
• Strong problem solving and deductive reasoning skills.
• Creativity and curiosity with the ability to learn and apply new concepts quickly.
• Excellent documentation skills.