Miro is a horizontal product covering various complicated use cases. Potentially it enables deep personalisation and automation of the user flow and communication to deliver more value to customers and lead to extensive usage and growth. This strategy requires the utilisation of data science/machine learning approaches. We are looking for a person who is able to lead the data science team aiming to convert data to product features.
Product development areas that aim to benefit from ML
- search and content discoverability
- users’ journey optimisation (smart communication and content recommendations)
- cross-device smart experience (pictures / drawing recognition)
- elaboration of new “smart analytics and insights” feature for enterprise customers
About the team
Data science team is a small resilient group of experienced professionals providing ML support for major product teams. The team deeply interacts with the key internal stakeholders in order to stay synchronised and proactively deliver solutions to the product teams and therefore add value for the end users. It is distributed across two hubs - Amsterdam and Perm.
What you'll do
- Provide technical and people leadership to growing data science team (2-5 DRs within first year)
- Contribute to the product strategy and roadmap with understanding what ML approaches could bring significant value for our users’ experience, product performance and business metrics.
- Work with the product teams (especially Canvas Core, UX, Enterprise and Growth) elaborating new ML-driven features.
What we expect
- 3+ years of leading data science / machine learning teams (preferably in the product companies)
- Proven experience in efficient deployment of ML to production
- Expertise in statistics / algorithmic ML / deep learning
- Knowledge of data and image processing
- English skills at upper intermediate level (or higher)
- Strong understanding of how business works
- Great communication skills (able to explain cross-functionally sophisticated things in a simple way)
Our technological stack, infrastructure, tools
- Standard Python data-science stack (Pandas, Numpy, Sklearn etc.).
- Deep learning frameworks: Pytorch / Tensorflow.
- Data Infrastructure: Spark, Hadoop, Hive, Kafka, PostgreSQL, Presto, Looker.
- Git, Jira, Confluence, Linux.
What’s in it for you
- Ability to bring state-of-the-art data science to the amazing product and get measurable impact on user experience and business metrics
- Opportunity to work in a highly productive and passionate atmosphere, to learn from smart peers as well as share your knowledge with them
- Stock option program
- Medical Insurance coverage
- Travel allowance for commute
- Lunch, snacks & drinks provided
- Team outings and collaboration
- Flexible time off
- Join our inspiring workplace at TQ Central