Data Science Manager

As a Data Science Manager at Rockerbox, you will lead and mentor a team of data scientists while also engaging in hands-on development for testing, Marketing Mix Models, and multi-touch attribution methodologies. You will drive best practices, collaborate across functions, and identify opportunities to integrate AI and machine learning for enhanced insights.

The ideal candidate for the Data Science Manager role at Rockerbox should possess 7+ years of experience in data science, analytics, or machine learning, including 1-2 years in leadership. Proficiency in Python, SQL, and cloud data platforms, along with a strong understanding of statistical modeling, causal inference, and related techniques, is essential.

Rockerbox is committed to the growth of its employees, offering a $1000 annual training stipend along with opportunities to explore both technical and business domains. We foster an environment where professional development is actively supported, enabling our team members to continuously enhance their skills.

As a Data Science Manager at Rockerbox, you enjoy a remote-first work environment, unlimited PTO, and a comprehensive health, vision, and dental insurance package. Additional benefits include 10 paid holidays, a traditional and ROTH 401(k), and parental leave, ensuring a well-rounded work-life balance.

The culture at Rockerbox is fast-paced, collaborative, and results-driven. As a Data Science Manager, you'll join a supportive team that values innovation and teamwork while also having the freedom to see your ideas develop and bring impactful contributions to life.

A successful approach to leading a data science team involves fostering an environment of collaboration and continuous learning. It's crucial to set clear goals, provide mentorship, and encourage team members to share their insights and experiences. As a Data Science Manager at Rockerbox, I would prioritize open communication and supporting my team's professional growth.

When discussing my experience with Marketing Mix Modeling (MMM), I would highlight specific projects where I developed MMM frameworks that helped our clients make informed marketing budget decisions. I would emphasize my ability to harness historical data and analyze performance metrics while addressing how MMM drives efficiency and allocation of resources.

I find regression analysis, Bayesian modeling, and time-series analysis to be particularly effective in a variety of scenarios. Each technique has its strengths depending on the context; for example, regression is great for understanding relationships, while time-series models handle temporal dynamics well. As a Data Science Manager, I ensure that my team is proficient in applying these techniques appropriately.

Validation of data models can be achieved through several techniques such as cross-validation, A/B testing, and back-testing. Each technique helps to confirm whether models are accurate and reliable. I focus on ensuring model robustness and continuously check that our methodologies align with the latest industry best practices.

When developing multi-touch attribution models, I consider aspects such as data quality, integration across marketing channels, and the specific objectives of our clients. A good understanding of the marketing funnel and customer touchpoints is essential to accurately gauge channel effectiveness and allocate resources accordingly.

I once faced a major challenge when analyzing inconsistent data from various sources. To overcome this, I implemented a rigorous data cleaning and validation process. Additionally, I coordinated with cross-functional teams to establish better data collection practices, ultimately leading to more reliable results.

Effective communication is key in any cross-functional role. I advocate for regular touchpoints and collaborative meetings to discuss data-driven insights while ensuring that complex concepts are translated into digestible information for stakeholders across marketing and engineering teams. Tailoring my communication style based on the audience fosters better understanding and collaboration.

To measure the success of our data science initiatives, I track metrics such as model accuracy, ROI from implemented models, time to deployment, and user adoption rates. These metrics provide insight into not just the technical performance of our models but also their practical impact on business objectives.

Staying updated in data science is vital due to its ever-evolving nature. I regularly read research papers, subscribe to relevant industry newsletters, attend webinars, and participate in professional communities where I can share knowledge and learn from peers. At Rockerbox, I also encourage my team to pursue ongoing education and share insights.

My approach to implementing AI and machine learning in marketing strategies starts with identifying key business challenges and data opportunities. I prioritize clear problem definitions and select appropriate algorithms that align with our objectives. Collaborating with marketing teams ensures that our models drive actionable insights that can optimize decision-making.