Senior Manager, Data Science

INTRO

We’re looking for an inspirational leader that understands and respects the honorable burden of leading fellow brilliant minds. Someone that will have a say in how our data science team executes and how we stay true to our values.

Your mission will be to run a world-class, highly efficient, and business-impact-driving data science team. Under your leadership, this team will balance:

Delivering applicative DS models that power our market-leading innovative products.

Research Lab functionality through DS, allowing the Data Org’s proactive identification of thematic opportunities for the business.

This is a unique opportunity to shape the strategy, methodology, and execution patterns of this vital data function. And lead the team and data culture at a global, data-driven company that creates worlds beyond gameplay and brings communities together.

EXPECTATIONS

• Serve as a Heart-First, People-First, Leader
  • Exemplify the values we live by. Know and care for the people in your teams out of the understanding that, as a leader, you work for them.
  • Nurture a blameless culture. Inspire, develop, and guide your team members to be the best that they can be.
  • Formalize your team’s identity to one everyone wants to be a part of.
• Excel as a Senior Manager
  • Build the processes that make us run efficiently. That automates the ordinary so we can focus on the extraordinary.
  • Be a champion of best practices for project management, and find the right recipe that makes your teams demonstrate high performance.
  • Prioritize pragmatically, communicate effectively, and know when to juggle and when to focus.
• Business & Data Focus
  • Know the data - Ask ALL the whys, relentlessly, until you know it inside-out. Instill that same rigor in your teams and push context to the entire Data Org.
  • Bring Business Value - Obsess about designing DS products that serve the business needs and provide empirical lasting value. You’re not here to produce models but to create measurable quality and impact.
• Be the Face of Your Teams
  • Create strong bonds & true partnerships with your stakeholders at the product, company leadership, and data leadership level.
  • Translate DS needs from strategic conversations to the day-to-month actions your teams will take.
  • Tell the stories of your teams’ deliverables, know how important it is to create awareness, and bridge the gap between non-technical people and what your team does
• Leadership Team Member
  • Demonstrate a servant-leadership mindset. Passionately deliver people, process, and culture-enabling products to our team members. Consistently.
  • Be willing and able to continuously be expected to learn and improve as a leader and a manager. Challenge and be challenged to develop together.
  • Be deliberate about our leadership team’s culture. Spend time with your fellow leaders, laugh together, cry together, and be who you really are.

Requirements

EXPERIENCE

• Data Science Lifecycle
  • You have experience in building towards the bigger picture of the ecosystem you’re working in: adding to Data Quality, Monitoring/Observability tools, etc. You know your code will not exist in a void.
  • You’ve participated in shaping a CI/CD process of a data science portfolio before. It’s not only about taking a model to production; it’s about maintaining many of them in a constant optimal state.
  • You’ve learned and have honed your method of how you pragmatically identify and prioritize the most promising hypotheses.
  • You have examples of every business metric you’ve moved through DS, as this is the best way you’ve learned to measure your impact.
• Science Hands-On
  • You have a rich experience with many EDA techniques. You know how to use the right one for a specific problem.
  • You have a track record of end-to-end model delivery by yourself. Developing, training, deploying, you’ve been through it all and know how to navigate the lifecycle efficiently.
  • You have a large toolbox you’ve been assembling for years, from R/Python, through SQL, statistical methods, distributed "big data" technologies (e.g., Spark, Hive, BigQuery, redshift), GCP/AWS/Azure ecosystems and their different SaaS experience. And you know when to use the right tool for the job and not the other way around.
• Stakeholder management
  • You are proficient in translating data science efforts into business language, you understand that “if you can’t explain it simply, you don’t really understand it”
  • You spent y