Role Description
The opportunity
At Unity, our Inclusion is driven by one overarching framework : Empathy, Respect, and Opportunity. In a collaborative, fast-growing environment, we are solving hard problems, and enabling the success of our community.
At Unity, you will make a difference. In the Unity Ads Applied Research team, we envision and build systems that help creators capture the value they build!
Come join Unity our Ads Applied Research team to help drive the overall Ads strategy for Unity's products and services. In this position you'll be driving our applied research initiatives to improve the optimization and machine learning powering our ad networks, market place and audience tools.
What you’ll be doing
As a data science expert, you will guide and drive the application of machine learning solutions across the whole organization.
Your role will be to :
- Translate business problems into a design and implement advanced machine learning models and algorithms.
- Follow up on the latest machine learning research to improve Unity’s Ad platform Machine Learning capabilities
- Form and validate hypotheses on new aspects of modeling and potency of new datasets for ML.
- Drive optimization hypotheses by analyzing the drivers for impactful machine learning and arguing the hypotheses and success for the next steps of development.
- Making requirements and communicating data and machine learning capability needs to the product and engineering teams.
- Driving the overall direction of machine learning future for Ads
What we’re looking for
We are looking for a candidate with experience in guiding machine learning development via revolutionary steps to keep Unity Ads at the
- PhD in Computer Science, Mathematics, Statistics, or a related technical field or equivalent experience on interpreting and translating research to applications.
- Experience successfully taking research and turning it into concrete product innovations with impact.
- Expertise with modeling frameworks ( Tensorflow, PyTroch) and understanding of model architectures and mechanisms.
- Ability to explain machine learning and interpret the modeling efficacy for further improvements.
- Capable of conducting analysis, statistics and data literacy to deduct where the challenges and opportunities lie.
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