Lead the team to enhance current Native Ads product performance by experimenting with current and new ML models
Participate in and challenge your team's systems architecture and modeling approaches, making the appropriate trade-offs based on metrics, quality, performance and cost, aligned with the team’s tech strategy
Grow the technical expertise of your team through technical leadership and advocating for sound practices
Identify, advocate for, and lead critical initiatives to advance technical and product performance in Native Ads
Manage a team of ML, DE, BE and Web engineers to develop, maintain, and own business-critical models that serve the Music Promotion business
Collaborate with a multi-functional, agile team, spanning user research, design, data science, product management, and engineering to build new product features that advance our mission
Foster a healthy, collaborative and productive engineering team in line with our values
Develop the team’s resilience amid fast change and ambiguity, supporting the team in building confidence in products and tech through prototyping and experimentation
Grow people and the team through hiring, coaching, mentoring, feedback, process and continuous improvement efforts
Requirements
You have a strong background in statistics/ML/AI technologies and their application to consumer products
You are invested in balancing developing thriving engineers and helping teams achieve significant business impact
You thrive in ambiguity, balancing tech health with speed of impact and learning, and are able to help a team incorporate new information as it emerges
Your strong product intuition allows you to efficiently connect the dots between multiple systems at Spotify
You have experience or a strong interest in emerging agent technologies and generative recommender systems
You aim to build capabilities that deliver a great, safe, and trusted experience to our customers and Spotify users
You understand the challenges of deploying and monitoring models in production, and can translate product metrics into model improvement strategies