Head of Data Science, Hungary

Key Skills

Data science techniques, Machine learning techniques

Job Description

We are seeking a Head of Data Science Platforms (HDSP) who loves developing people as much as they love developing models and catching fraud. They should believe quality and velocity are not at odds, that you can build systems which ensure high quality while moving fast.

The responsibilities of the Head of DS fall into three categories:

• Attract, hire, and develop great people. To have great ideas, you must have a lot of ideas. To execute on those ideas, you must have a lot of great people. The HDSP is responsible for building a team that generates great ideas and has the skills to execute on them.
• Create a culture that rewards curiosity, cooperation, and continuous improvement. We get better through thousands of small experiments and improvements executed over a long period of time by many people.
• Remove blockers. We hire motivated people who want to move quickly and produce. Blockers come in the form of technical debt, miscommunication, or skill deficiencies. The HDSP should be relentlessly focused on eliminating blockers that stop people from moving quickly, whether that means prioritizing tech debt, developing consensus, or teaching new skills.

This role reports to the SVP of Data Science. 

Responsibilities

• Develop a culture that is comfortable with decentralized decision-making and which builds systems that are optimized for change and adaptability.
• Establish resourcing and talent plans to deliver against a rapidly changing roadmap.
• Crisply communicate technical concepts to non-technical internal stakeholders as well as customers.
• Establish and lead a Budapest team; enable smooth collaboration across offices and time zones.
• Collaborate with the other VPs in R&D (the VPs of Product and Engineering), as well as other members of the Senior Leadership Team to develop a common roadmap and execution plan.
• Build and maintain a Feature Engineering and ML Platform to enable faster feature generation, the use of larger training sets, and more frequent model iteration.
• Support with building, maintaining, and executing our roadmap
• Close the loop from tagging of MOs, to new feature generation, to model development or rules
• Establish common hiring and career development standards and oversee their implementation in Budapest.

LI-Hybrid

Benefits:

• Stock Options
• Annual Performance Bonus or Commissions
• Pension matched up to 3%
• ‘Day one’ access to great health insurance scheme
• Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads)
• Paid team social events
• Headspace Benefits
• Dedicated learning budget through Learnerbly