Required YOE: 10+ Years (NON-NEGOTIABLE)
REQUIRED: Hands-on experience with Ranking & Recommendation Algorithms
Summary:
The main function of a Ranking Data Scientist is to produce innovative improvements to machine learning models by leveraging exploratory data analysis from complex and high-dimensional datasets.
Job Responsibilities:
Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.
Leverage computer vision, feature extraction, and machine learning algorithms to determine the most important aspects that drive shareability of content.
Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data.
Generate and test hypotheses and analyze and interpret the results of product experiments.
Work with product engineers to translate prototypes into new products, services, and features and provide guidelines for large-scale implementation.
Provide Business Intelligence (BI) and data visualization support, which includes, but limited to support for the online customer service dashboards and other ad-hoc requests requiring data analysis and visual support.
Metric movement investigations for key recommendation products
Leadership reporting on a weekly basis
Experimentation review and analysis
Ad-hoc team and leadership data requests
Target audience Identify target users for our products to inform the short-term and long-term strategy
Skills:
Fluency with SQL and Python.
Hands-on experience building models with PyTorch or TensorFlow libraries.
Experience training and/or using computer vision models.
The ability to communicate effectively in writing, including conveying complex information and promoting in-depth engagement on course topics.
Experience working with large datasets.
Education/Experience:
Master s degree in Mathematics, Statistics, a relevant technical field, or equivalent practical experience (PhD preferred).
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