Principal Data Scientist

Your responsibilities:

- Lead and take part in end-to-end ML (Machine Learning) projects deployments that require feasibility analysis, design, development, validation, and application of state-of-the art data science solutions.

- Need to deliver solutions to production through the software development lifecycle

- You will have to push the state of the art in terms of the application of data mining, visualization, predictive modelling, statistics, trend analysis, and other data analysis techniques to solve complex business problems including recommender systems, Contact-centre optimization, Customer sentiment modelling, customer life-cycle modelling, Spare parts inventory optimization, spare parts pricing, warranty, After market channel effectiveness

- Leverage and enhance applications using Natural Language Processing and Deep Learning for use cases including text mining, speech and object recognition

- Provide leadership to set up world-class ML lifecycle management processes.

- Write production ready code and deploy real time ML models; expose ML outputs through APIs (Application Programming Interface)

- Partner with Data/ML engineers and vendor partners for input data pipes development and ML models automation

Your qualifications:

- For Principal Data Scientist: Over 10+ years of Applied Machine learning experience in the fields of Machine Learning, Recommender Systems, Statistical Modelling, Predictive Modelling, Text Mining, Natural Language Processing (NLP), Deep learning and Optimization.

- Educational Experience: PhD in Math, Computer Science, Statistics or related field or Masters in Math, Computer Science, Statistics or related field

- Applied experience with Machine Learning and Deep Learning on Large Datasets.

- Have a significant research expertise/accomplishment, theoretical depth and business experience in the e-commerce, customer analytics and marketing analytics areas such as build recommendation Engine/ Price optimization / Personalization / market mix modelling etc.

- Deep understanding of text representation and modelling techniques such as word-embedding, sub word embedding, Transformer architecture, BERT, NER, Intent Extraction, Entity Sentiment Analysis and Language Detection

- End-to-end hands on experience in data gathering, preparation, feature engineering, model building, validation, and productionizing at scale.

- Expert Python Programmer: SQL, extremely proficient with the SciPy stack (e.g. NumPy, pandas, scikit learn, matplotlib);

- Proficiency in work with open source deep learning libraries like TensorFlow, Keras, Pytorch

- Knowledge of the Big Data Ecosystem: (Apache Spark, Hadoop, Hive, EMR, MapReduce)

- Proficient in Cloud Technologies and Service (Azure Databricks, ADF, Databricks MLflow)