Senior Machine Learning Scientist - Enterprise at Whip Media
🇺🇸 United States › New York › New York (Posted Jan 19 2022)
Whip Media is transforming the global content licensing ecosystem with a market leading enterprise software platform that centrally connects data, processes and teams throughout the digital distribution journey. Powered by predictive insights and proprietary data, we enable the world’s top entertainment organizations to efficiently distribute, control and monetize their TV and movie content to drive revenue and direct-to-consumer growth.
Whip Media is adding a Machine Learning Scientist to its Data Team in Santa Monica but, for the right candidate, we’re open to hiring in cities where our other Whip offices exist (New York & London) or remote work entirely. Our main goals as a team are to understand what drives demand for content among audiences around the world, and to use that understanding to transform the global content licensing ecosystem. You will be a primary contributor to our Machine Learning services and initiatives, translating our goals and ideas into data products through the creation and deployment of scalable, novel algorithms. We are looking for talented practitioners who love to play with the latest and greatest technologies, yet who deliver reliable and efficient systems.
What will you do?
Translate our business strategy into a Machine Learning vision, then digest that vision into short- and long-term plans of execution.
Create, maintain, and iterate on our Machine Learning products that drive business-to-consumer apps, used by millions of users each month and business-to-business products, which back billions of dollars worth of decision-ing each month.
Work closely with the Data Engineering and Data Insights teams to ensure efficient extraction of signals from our extensive data sets.
Communicate findings with company leadership and external clients.
What do you need?
5+ years working as a Senior/Lead Data Scientist
Experience creating data products that successfully address large scale-business challenges.
Experience with all phases of the ML lifecycle, from ideation and experimentation to deployment in production environments.
Experience with a variety of problem domains, including: timeseries forecasting, NLP, recommendation systems, etc.
Strong Python programming ability.
Proficiency with a variety of data processing and ML toolkits, including: PySpark, NumPy, Pandas, TensorFlow, Keras, scikit-learn, LightGBM, XGBoost, etc.
Ability to write complex SQL queries, and a strong understanding of relational databases.
PhD degree in Computer Science, Mathematics, Statistics or a related technical field preferred; MS required.
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