Lead Data Scientist

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Logicloop

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Job Description

• 8 - 10 years of total relevant experience with hands-on programming skills in Python, R, etc. • Experience on minimum End to End production rollouts / implementations of Data Science and analytics projects • Minimum of 4 years in technical execution roles in Data Science, Data Analytics & Machine Learning projects. • Experience on Deep learning techniques (Sentiment Analysis, Text Mining, Entity Extraction, Document Classification, Topic Modelling, Natural Language Understanding (NLU) and Natural Language Generation (NLG)) • Experience in Web Scraping or data extraction through Selenium / Scrapy or other frameworks and related libraries. Knowledge of advanced scraping techniques (like overcoming captcha, proxy, browser fingerprinting, bot detection bypassing, etc) • Experience on Working knowledge in various DBs (MySQL, HBase, MongoDB), message queues & Web Restful APIs. • Expertise in open-source NLP toolkits such as CoreNLP, OpenNLP, NLTK, gensim, LingPipe, Keras, Tensorflow, Mallet, ML/math toolkits such as scikit-learn, MLlib, Theano, NumPy, etc. • Experience of development, testing & deployment of Machine learning/deep learning projects in the on premise /cloud platforms. • Experience in diverse Data Science modelling including deep learning and reinforced learning. • Experience on ETL tools like Talend, Data stage • Experience in Big Data and related technologies: Map/Reduce, Hadoop, Hive, Spark, SQL, Mongo DB, HDFS etc. • Experience in Analytics - Reports and Dashboards Industry Preferably from a Technology, ITES, Products, Professional SI organization with a focus on BFSI. Responsibilities • He/she will be a technical hands on / delivery role responsible for executing challenging and complex data science and engineering projects • Technically lead a team of data scientists and engineers for expert technical vision, analytical and business intelligence • Responsible for data science professional design, development and deployment in the use of modern data science tools. • Performs deep analyses, builds models/ algorithms and evaluate the models to understand business objectives • Analyse large amount of raw information to find patterns, build data products and models to extract valuable business insights. • Build, develop, coach and lead a team of data professionals in the area of Data Engineering, Business Intelligence, Analytics, Data Science, Experimentation, AI & ML. • Perform statistical and exploratory data analysis • Research and build appropriate Ai/ML algorithms and review the (code) implementation of team members • Identify valuable data sources and architect/automate ETL strategies • Develop and maintain optimal data pipeline architecture • Analyse large amounts of information to discover trends and patterns .


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