Lead Data Scientist

• As a Lead Data Scientist, Responsible and full ownership for the deliverables with greater defined quality standards with defined timeline and budget
• Maintain good relationship with internal and external stakeholders. Demonstrate solidarity, curiosity, and constructiveness towards stakeholder needs.
• Watch external industry, research, and analysis to give insight to markets on industry trends and sales targets
• Good Technical mentor, Guide the team and review their work, Team player having collaborative mindset to produce the outcome in collective manner
• Able deliver the deliveries in Fast/Agile/Scrum Mode
• Able to take and drive the new challenges and initiatives in the project
• Ability to predict the project risks and provide the necessary resolutions.
• Model design, feature planning, system infrastructure, production setup and monitoring, and release management.
• Develop, process, cleanse and enhance data collection procedures from multiple data sources
• Conduct deliver experiments and proof of concepts to validate business ideas and potential value.
• Test, troubleshoot and enhance the developed models in a distributed environments to improve its accuracy.
• Work closely with product teams to implement algorithms with R and/or Python.
• Design and implement scalable predictive models, classifiers leveraging machine learning, deep learning.
• Facilitate integration with enterprise applications using APIs to enrich implementations.
• Having Good Logical/Analytical/ problem-solving skills with Innovative mindset to solve the problem in Innovative way and Healthy attitude towards challenging current way of doing things
• Having Good skill on CI/CD Design/object oriented / Microservice principles
• Ability to have thought process on failure/fault tolerance in distribute system design and development
• Ability to work in Startup-mode of product development with building MVP product.
• Ability to predict the project risks and provide the necessary remediations.

Profile required

6-8 years in applying AI/ML principles to real world applications

Broad NLP knowledge: tokenisation, part-of-speech tagging, dependency parsing, syntactic parsing, word sense disambiguation, topic modeling; contextual text mining, Word embedding

Broad Computer Vision knowledge:

o Construction, Feature detection, Segmentation, Classification

o object detection, tracking, localisation, classification, recognition, scene understanding

• Exposure to Deep Learning - CNNs, LSTMs, network architecture, network tuning, transfer learning, multi-task learning
• Broad Machine Learning experience - Algorithm Evaluation, Preparation, Analysis, Modeling and Execution.
• Exposure to Open source NLP libraries eg NLTK, Regex, Stanford NLP, OpenNLP/CoreNLP
• Very strong grasp of IT concepts with a strong algorithms/data structures background
• Demonstrated history of building prototypes to win business confidence.
• Experience in using Keras,Tensorflow, Caffe and/or other neural network development frameworks.
• Experience with common data science toolkits, such as Scikit, NumPy, R libraries - Excellence in at least one of these is mandatory.
• Proficiency with any one NoSQL databases such as MongoDB, Cassandra, HBase.
• Should have prior experience in developing APIs, services using either C# or Java.
• Should have good awareness on entire machine learning/ predictive modeling implementation