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Roles and Responsibilities
End to end ranking of search results
Document Text/Passage extraction and understanding
Compliance and Privacy aware Machine Learning training pipelines at scale
Personalization of search results
Related Query Recommendation systems
Question Answering Recommendation systems
To ensure knowledge up-gradation and work with new technologies so that the solution is current and meets quality standards and the client requirements.
To gather specifications and deliver solutions to the client organization based on understanding of a domain or technology.
To train and develop team so as to ensure that there is an adequate supply of trained manpower in the said technology and delivery risks are mitigated.
To ensure process improvement and compliance in the assigned module, and participate in technical discussions or review.
To prepare and submit status reports for minimizing exposure and risks on the project or closure of escalations.
To create work plans, monitor and track the work schedule for on time delivery as per the defined quality standards.
To develop and guide the team members in enhancing their technical capabilities and increasing productivity.
Required Technical and Professional Expertise
Data Scientist with 5+ years of work experience in statistical computing, machine learning and advanced data analysis.
Should be proficient in Python and/or R and should have hands on experience in using open source data science packages including NumPy, Scikit-learn, Pandas, Matplotlib, Statsmodel, CRAN
Should have strong understanding of how popular algorithms work, not just implementation of libraries. Specific algorithms of interest includes random forest, K-Means, SVM, Timeseries, ARIMA. Experience in deep learning techniques like RNN, CNN etc will be an added advantage.
Should have experience in Text Analytics and NLP based solutions. Should have experience using packages like NLTL, word2vec and OpenNLP. Experience in developing recommender systems and/or natural text based information retrieval systems will be an advantage.
Should have experience in one or more of the following domains: credit risk analytics, fraud/anomaly detection, supply chain analytics and forecasting, customer analytics, natural text matching,
Should have experience in productionizing Machine Learning models. This includes working on Linux environments and with various model management tools.
Should have working knowledge of cloud platforms like AWS and Azure within the context of building ML models and deploying them on these platforms.
Exposure to data engineering and big data platforms will be an advantage.
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