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As a Data Scientist, you will use your expertise in Data Science/Machine Learning to tackle exciting problems such as: addressing fairness and bias in recommendation systems, using reinforcement learning to optimize long-term engagement metrics, using transformer models in recommendation systems, building multimodal models, etc.
Key Responsibilities:
Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools.
Effectively communicate the analytics approach and how it will meet and address objectives to business partners.
Advocate and educate on the value of data-driven decision making; focus on the “how and why” of solutioning.
Lead analytic approaches; integrate solutions collaboratively into applications and tools with data engineers, business leads, analysts and developers.
Create repeatable, interpretable, dynamic and scalable models that are seamlessly incorporated into analytic data products.
Use data science and machine learning methods to bootstrap new functionalities.
Handle competing requests from a range of data customers.
Tackle complex problems that arise in production and find customer-centric solutions.
Engineer features by using your business acumen to find new ways to combine disparate internal and external data sources.
Share your passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods and standards.
Collaborate, coach, and learn with a growing team of experienced Data Scientists.
Stay connected with external sources of ideas through conferences and community engagements.
Requirements:
Bachelors Degree in Data Science, Computer Science, Applied Mathematics, or Statistics
5+ years of Data Science and Machine Learning experience required
Proficiency in Python or R. Ability to write complex SQL queries
Proficiency with Machine Learning concepts and modeling techniques to solve problems such as clustering, classification, regression, anomaly detection, simulation and optimization problems on large scale data sets
Experience with AWS is a plus
Ability to implement ML best practices for the entire Data Science lifecycle
Ability to apply various analytical models to business use cases (NLP, Supervised, Un-Supervised, Neural Nets, etc.)
Exceptional communication and collaboration skills to understand business partner needs and deliver solutions
Bias for action, with the ability to deliver outstanding results through task prioritization and time management
Experience with data visualization tools — Tableau, Power BI, etc. preferred
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