Basic Qualifications:

• • Level: Senior Manager
  • Minimum Year(s) of Experience: 10 – 14 years of overall experience with at least 5 years dedicated advanced analytics and ML
  • Level of Education/ Specific Schools: Graduate/Post Graduate from reputed institute(s) with relevant experience
  • Field of Experience/ Specific Degree: B.Tech./M.Tech/Masters Degree or its equivalent /MBA
  • Preferred Fields of Study: Computer and Information Science, Artificial Intelligence and Robotics, Mathematical Statistics, Statistics, Mathematics, Computer Engineering, Data Processing/Analytics/Science
  • Knowledge Required:
    • Demonstrates intimate abilities and/or a proven record of success in the following areas:
      • Understanding statistical or numerical methods application, data mining or data-driven problem solving
      • Demonstrating thought leader level abilities in the use of statistical modelling, algorithms, data mining and machine learning algorithms
      • Demonstrating proven delivery within a number of large scale projects
      • Demonstrating ownership of architecture solutions and managing change
      • Understanding business development such as client relationship management and leading and contributing to client proposals
      • Communicating project findings orally and visually, to both technical and executive audiences
      • Developing people through effectively supervising, coaching, and mentoring staff
      • Demonstrated contributions in firm development and knowledge building activities such as recruitment, intellectual capital development, staffing, marketing, branding
      • Leading, training, and working with other data scientists in designing effective analytical approaches taking into consideration performance and scalability to large datasets
      • Manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
    • Demonstrates intimate abilities and/or a proven record of success in the following areas:
      • Demonstrated ability to continuously learn new technologies and quickly evaluate their technical and commercial viability
      • Demonstrating thought leader-level abilities in commonly used data science packages including Spark, Pandas, SciPy, and Numpy
      • Leveraging familiarity with deep learning architectures used for text analysis, computer vision and signal processing
      • Developing end to end deep learning solutions for structured and unstructured data problems 
      • Developing and deploying AI solutions as part of a larger automation pipeline 
      • Utilizing programming skills and knowledge on how to write models which can be directly used in production as part of a large scale system
      • Understanding of not only how to develop data science analytic models but how to operationalize these models so they can run in an automated context 
      • Using common cloud computing platforms including AWS and GCP in addition to their respective utilities for managing and manipulating large data sources, model, development, and deployment
      • Experience conducting research in a lab and publishing work is a plus

• Experience with following technologies: 

• Programming: Python (must) , having experience in R is a plus
• Machine Learning Libraries: Python (Numpy, Pandas, scikit-learn, gensim, etc.), TensorFlow, Keras, PyTorch, Spark MLlib, NLTK, spaCy)
• Visualization: Python (like Matplotlib, Seaborn, bokeh, etc.), third party libraries (like Power BI, Tableau) 
• Productionization and containerization technologies (Good to have): GitHub, Flask, Docker, Kubernetes, Azure DevOps, GCP, Azure, AWS.

Role and Responsibilities:

• Leadership:
  • Leading initiatives aligned with the growth of the team and of the firm
  • Providing strategic thinking, solutions and roadmaps while driving architectural recommendation
  • Interacting and collaborating with other teams to increase synergy and open new avenues of development
  • Supervising and mentoring the resources on projects
  • Managing communication and project delivery among the involved teams
  • Handling team operations activities
• Quickly explore new analytical technologies and evaluate their technical and commercial viability
• Work in sprint cycles to develop proof-of-concepts and prototype models that can be demoed and explained to data scientists, internal stakeholders, and clients
• Quickly test and reject hypotheses around data processing and machine learning model building
• Experiment, fail quickly, and recognize when you need assistance vs. when you conclude that a technology is not suitable for the task
• Build machine learning pipelines that ingest, clean data, and make predictions
• Develop, deploy and manage production pipeline of ML models; automate the deployment pipeline
• Stay abreast of new AI research from leading labs by reading papers and experimenting with code
• Develop innovative solutions and perspectives on AI that can be published in academic journals/arXiv and shared with clients