Job Description

Engage with multiple teams and assist in the implementation of the proposed design, delegate work items to team members, monitor project status and review outcomes.
Lead a team of data scientists and analysts, to brainstorm ideas on modeling techniques, and guide junior members of the team as required.
Participate in scrum meetings to monitor the progress of work items, recommend alternate methods (if available) to complete project tasks.
Track client implementations and work with delivery teams to identify gaps in product features, assess feedback around model accuracies and relay the same to stake holders from within product team.
Collaborate with client teams to on-board data, build models and score predictions.
Build project proposals, contribute to designing of Data Science modules and creating prototypes across variety of ML projects
Research unfamiliar methodologies, techniques to fine tune existing models in the product suite and, recommend better solutions and/or technologies.
Improve features of the AIQ product to include newer machine learning algorithms in the likes of product recommendation, real time predictions, fraud detection, offer personalization etc.
Demonstrate a basic understanding of different machine learning concepts such as Regression, Matrix Factorization, K-fold Validations, and different algorithms such as Decision Trees, Random Forrest, K-means clustering.

Qualifications
Bachelors degree in a quantitative discipline (e.g., Statistics, Economics, Mathematics, Marketing Analytics) or significant relevant coursework
Proficiency with a deep learning framework such as TensorFlow or Keras; minimum 7 years of experience
Exposure to CNN, RNN neural networks based professional projects solving problem in the likes of time series data and image data.
In-depth understanding of LSTM concepts and be able to implement advances techniques such as stacked, bidirectional and seq2seq
Demonstrated proficiency in PYTHON and BIG DATA technologies and the proven ability to program in big data/cloud technologies such as AWS & SPARK; minimum 3 years of experience
A Deep understanding of Recommender Systems and applications around real-time predictions
Experienced with machine learning algorithms such as logistic regression, random forest, XG boost, KNN, SVM, neural network, linear regression, lasso regression and k-means.
Desirable Qualifications:
Advanced degree (Masters/PhD) in Statistics, Economics or other quantitative discipline
Knowledge and understanding of AWS Sagemaker
Working experience in CI/CD tools such as GIT & BitBucket
Prior experience of leading a group of junior data scientists will be preferred
Education: Any Graduate
Industry: Advertising/PR/Event Management, Entertainment/Media

Skills Required