Sr. Manager, Data Science

The Role

Mejuri is searching for a Sr. Manager, Data Science to oversee the Customer, Commercial, and Operations Data Science and Analytics teams. Reporting into the head of Data, this individual will be responsible for strategizing and driving execution against the data science & analytics roadmap, developing strategic business frameworks, deploying production models, driving the cross-company insights program, and hiring, managing, and coaching a team of data scientists and business analysts. This opportunity is ideal for someone who has experience growing data science & analytics teams, is comfortable working closely with executives, and is looking to provide high-value work within a small and agile data team while seeing the impact of their work in real-time.

What You'll Do

• Leadership: Strategize and drive execution against the data science & analytics roadmap, own collaboration with cross-functional stakeholders, communicate insights to the senior leadership, and establish operational workflows that optimize impact (engagement, prioritization, delivery).
• Data/ML Applications: Own delivery of external models (e.g. pricing, content-recommendation), strategic business models (e.g. revenue growth, segmentation), and insight applications/dashboards.
• Insight Program: Drive execution and delivery of analytical deep dives that provide deep insight into customer behavior and business impact.
• Data Champion: Promote the application of data and best practices across the entire business.

What You Bring To The Team

• Management & Communication: Experience working with cross-functional stakeholders and managing a team, ability to manage long-term roadmap and prioritize competing efforts. Strong communication skills, both written and verbal, being able to explain complex technical information to a non-technical audience.
• Technical: Experience working with customer data platforms, event-level data, ML & feature engineering pipelines. Comfortable writing/reviewing code written in SQL, Python, R, or Scala, and leveraging machine learning frameworks & toolkits such as PyTorch, TensorFlow, Spark, or scikit-learn.
• Machine Learning & Analytics: Advanced understanding of machine learning & analytics applications and experience building & deploying models from the ground up.
• Experimentation: Experience designing & interpreting results of experiments across.