â Work on end -to -end data science lifecycle: from building proof -of concept models to
production -ready models & work with engineers to deploy them to production
â Execute and take ownership of deep learning projects end -to -end. Good exposure to
various deep learning algorithms and frameworks
â Should be able to effortlessly switch between roles of an Individual Contributor, team
member, and data science manager as demanded by each project.
â Managing analysts on the project - work distribution and timeline planning for the
projects, training and guiding the analysts
â Be able to manage business people and excellent in pitching your work
â Figure out gaps in existing products and adding intelligence to improve it
â Designing the frameworks in data sciences (R, Python, Azure ML, Amazon ML, Google
ML, Spark etc) to be used for relevant projects
â Timely interfacing with the vendors in resolving their asks and queries along with testing
and verifying their deliverables and refining them.
â Keep updated on latest research in AI and deep learning and find ways in which it can
be used to solve business problems
â Must be able to read research papers (eg: some novel architectures, training techniques,
etc.) and be able to implement it fully to solve our specific problems
Technical Skill Set
â Expert user of Python,
R and SQL
â Hands -on experience in handling unstructured (image, video, audio) and build
solutions using Deep Learning / Machine Learning
â Experience in implementing object detection, GAN’s. Knowledge of reinforcement
learning is a plus.
â Good grasp and detailed knowledge of any one deep learning library (PyTorch,
Tensorflow, Caffe etc.) is a must
â Experience with Data Visualization tools like Tableau, Microsoft Power BI and
SiSense
â Exposure to working on AI/Data Science/Analytics platforms likeMicrosoft AI,
Amazon AI, IBM WATSON, H20, Knime
â Solid business acumen to understand business objectives & dynamics and
possess excellent written and verbal communication skills for coordinating across
teams
â Experience in analysing complex problems and translating them to data science
algorithms with due attention to computational efficiency and testing at scale.
â Experience in machine learning, supervised and unsupervised:Forecasting,