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Intro
The job at PublicisGroupe involves contributing to an internal product library focused on prediction & recommendation, researching methodologies to enhance existing models, improving AIQ product features, collaborating with client teams, building machine learning models, and analyzing large datasets. The role requires a Bachelor's degree in a quantitative discipline, proficiency in deep learning frameworks like TensorFlow or Keras, experience with CNN and RNN neural networks, and proficiency in Python and big data technologies like AWS & Spark. Desirable qualifications include an advanced degree and knowledge of AWS Sagemaker.
Tasks
- Contribute to building an internal product library focused on solving business problems related to prediction & recommendation
- Research unfamiliar methodologies and techniques to fine-tune existing models in the product suite
- Improve features of AIQ product with newer machine learning algorithms
- Collaborate with client teams to onboard data, build models, and score predictions
- Participate in building automations and standalone applications around machine learning algorithms
- Analyze large datasets, perform data wrangling operations, apply statistical treatments, and engineer new features for machine learning models
- Run test cases to tune existing models for performance and define success criteria
- Demonstrate understanding of machine learning concepts and algorithms
Requirements
- Bachelor’s degree in a quantitative discipline or significant relevant coursework
- Proficiency with deep learning frameworks such as TensorFlow or Keras
- Experience with CNN, RNN neural networks, LSTM concepts, and implementation of advanced techniques
- Proficiency in Python and big data technologies like AWS & Spark
- Deep understanding of Recommender Systems and real-time predictions
- Experience with machine learning algorithms like logistic regression, random forest, XG boost, KNN, SVM, neural network, linear regression, lasso regression, and k-means
Benefits
- Career development opportunities
- Startup environment
- Flex hours
- Health care
- Team events
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