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Data Science Engineering Manager
2 months ago
We're seeking a seasoned Engineering Lead Manager, Data Science to spearhead our Machine Learning and Optimization organization. As a critical member of our team, you'll be responsible for the research, development, and production of our ML and data science algorithms and usage of our data pipelines.
Our systems process over 350 billion auction events, and our scale spans the globe. With transactions happening 24x7 in our global data centers, every second counts in our business. To achieve our mission, global efficiency and reliability are paramount.
Key Responsibilities- Technical Leadership: You'll chart a technical path for products, systems, and architectures, guiding your team with industry best practices.
- People Leadership: You'll lead a team of data scientists and machine learning engineers, mentoring and empowering them to deliver incredible impact.
- Handler of Broad Scope: You'll thrive in environments with multiple projects and initiatives, self-managing your time and your team's to achieve the highest impact.
- Driver of Outcomes: You'll establish a vision or hypothesis, setting a direction and guiding your team through execution, using data to inform pivots or validate assumptions.
- End-to-End Owner: You'll take pride in the products your team owns and delivers, ensuring quality, health, and outcomes, and leading your team in responding to issues.
- Organizational Coordinator: You'll foster a collaborative approach to delivery, leveraging relationships to unblock dependencies and celebrating successes.
- Bachelor's degree or higher in Mathematics, Computer Science, Engineering, or equivalent.
- Demonstrated leadership and management of 3-10 person teams.
- Passion for cutting-edge technologies, leading teams, and nurturing talent development.
- Extensive experience in visualization and explanatory articulation of data science approaches, test and production results.
- Experience with ML and data science platforms and technologies.
- Experience with building and managing ML data pipeline to support data science and machine learning initiatives.
- Knowledge of big data stack including Kafka, Hadoop, Spark, Scala, and Airflow, as well as data warehouse technologies such as Vertica.
- Knowledge of BI tools like Looker and/or Redash will be very helpful.
- Experience with Docker and Kubernates is a plus.
