For a complete understanding of this opportunity, and what will be required to be a successful applicant, read on.
The role
In this role, you will be responsible for :
Working with a variety of data sources provided by Magnify's customers and leveraging these to build models that predict future revenue expansion and churn;
identify drivers for key events in the customer lifecycle (renewal, expansion, cancellation, etc.); and enable accelerating product adoption and usage.
- Collecting and understanding customer needs, Magnify's vision, and stakeholder input to improve model accuracy and relevancy, and to develop new models and functionality to expand Magnify's products.
- Innovating and providing thought-leadership to the Magnify team around future data science and machine learning opportunities, including expanded use of generative AI.
- Partnering with the engineering team to build infrastructure that provides reliability, observability, and scalability to data science pipelines and model / product integrations.
Qualifications and Experience
Successful candidates are likely to have the following qualifications and experiences; we strongly encourage you to apply even if you don’t meet all of the items below.
- Masters in Computer Science or related field; PhD preferred.
- 7+ years of experience working as a data scientist in a high growth, startup environment.
- Strong knowledge and hands-on experience across machine learning, causal inference, experimentation, product analytics, revenue analysis, statistics, and / or optimization.
- Ability and comfort operating with ambiguity in an early stage startup environment in which you may need to "bootstrap" data science infrastructure to complete projects.
- Strong communication skills, both written and verbal, and the ability to successfully engage across audiences of varying backgrounds and technical proficiencies.
- Passionate about delivering for users and collaborating with teammates.
- Have a strong bias for action, a track record of moving quickly, and the ability to identify where and when scrappiness is the right approach versus those places where deeper rigor is required.
Seattle-area preferred; This role is remote eligible within the United States.
No third parties / recruiters please.
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