• 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