Epsilon is the leader in outcome-based marketing. We enable marketing that’s built on proof, not promises. Through Epsilon PeopleCloud, the marketing platform for personalizing consumer journeys with performance transparency, Epsilon helps marketers anticipate, activate and prove measurable business outcomes. Powered by CORE ID®, the most accurate and stable identity management platform representing 200+ million people, Epsilon’s award-winning data and technology is rooted in privacy by design and underpinned by powerful AI. With more than 50 years of experience in personalization and performance working with the world’s top brands, agencies and publishers, Epsilon is a trusted partner leading CRM, digital media, loyalty and email programs. Positioned at the core of Publicis Groupe, Epsilon is a global company with over 8,000 employees in over 40 offices around the world.

For more information, visit epsilon.com. Follow us on Twitter at @EpsilonMktg.

• Analyze large datasets, perform data wrangling operations, apply statistical treatments to filter and fine tune input data, engineer new features and eventually aid the process of building machine learning models.
• Collaborate with client teams to on-board data, build machine learning models and score predictions.
• Participate in building automations and standalone applications around machine learning algorithms to enable a “One Click” solution towards getting predictions and recommendations.
• Run test cases to tune existing models for performance, define criteria and thresholds for success by scaling the input data to multifold.
• Contribute and build an internal product library that is focused on solving business problems related to prediction & recommendation.
• Research unfamiliar methodologies, techniques to fine tune existing models in the product suite and, recommend better solutions and/or technologies.
• Improve features of the product to include newer machine learning algorithms in the space of product recommendation, real time predictions, fraud detection and offer personalization.

Minimum Qualifications:

• Bachelor’s degree in Computer Science / Statistics / Mathematics or significant relevant coursework
• Demonstrated proficiency in PYTHON (minimum 2 years of experience)
• Experience working with BIG DATA technologies and the proven ability to program in big data/cloud technologies such as AWS & SPARK
• Experience with traditional machine learning algorithms such as regressions, kNN, decision trees, random forest, XGBoost, SVM and neural networks.
• Understanding of deep neural networks, RNNs and LSTMs with exposure to a deep learning framework like Tensorflow or Keras

Desirable Qualifications: 

• Advanced degree (Master’s/PhD) in Statistics, Economics or other quantitative discipline
• A Deep understanding of Recommender Systems and applications around real-time predictions
• Knowledge and understanding of AWS Sagemaker
• Working experience in CI/CD tools such as GIT & BitBucket