Responsibilities:

• Design new machine learning models for the organization’s product suite
• Implement machine learning models and fine-tune them for different use cases.
• Solve complex business problems of global organizations by applying NLP, AI and Machine Learning techniques
• Systemic study of the latest research and industry developments in Artificial Intelligence. Leverage these developments to solve real-world business problems
• Work closely with Software Engineers & Data Scientists to build the data infrastructure that fuels the needs of multiple teams, systems, and products
• Automate manual processes, optimize data-delivery and build the infrastructure required for optimal extraction, transformation, and loading of data required for a wide variety of use-cases using SQL/ETL
• Design competitive AI/ML services and user experience for next-generation devices and create prototypes for demonstration
• Build realtime and batch analytics platform for analytics & machine-learning
• Design, develop, test, deploy, maintain and improve ML models/infrastructure and software that uses these models
• Create algorithms to extract information from a large data set to perform statistical analysis and analyze the same to create predictive models for consumer behavior

Technical Skills:

• Technical knowledge on existing AI/ML platforms, Statistical tools as R, Python, Saas (at least one technology platform)
• Understands Data very well and has extensive Data Modelling experience
• Deep understanding of real-time as well as batch processing big data technologies (Spark, Storm, Kafka, Flink, MapReduce, Yarn, Pig, Hive, HDFS, Oozie etc)
• Experience in developing applications that work with NoSQL stores (e.g., ElasticSearch, HBase, Cassandra, MongoDB, CouchDB)
• Practical experience using multivariate and/or ML methods for classification, clustering and estimation, such as Decision Trees, Random Forests, CART/CHAID, Extreme Gradient Boosting, Ridge Regression, PCA and SVM
• Should be an expert in handling various data types and structures: structured, unstructured, voice, image and video data, static versus streaming data. Extensive prior experience in integrating data, profiling, validating and cleansing data in Big Data platforms is a must
• Strong background with advanced mathematical concepts related to Probabilistic Models, Linear Algebra, Calculus, and Optimization
• Decent grasp of statistics concepts (mean, median, moments etc.)
• Good technical writing, reporting & communication skills
• Good experience with BigML APIs or frameworks such as Tensorflow

Preferred qualifications:

• Bachelors degree with a minimum of 2.5 years of experience in Artificial Intelligence, Data Science, Machine Learning, Deep learning and Natural Language Processing (NLP)
• Hands-on in Python or R and working knowledge of Data Science
• Expertise in using Neural Networks for complex pattern recognition
• Prior experience in working on Big Data environments
• Experience in Agile/Scrum development process