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.