Machine Learning – Manager; Diversity

Machine Learning – Manager (Diversity Candidates)

Any Location - Remote

• Level: Manager
• Minimum Year(s) of
  
  Experience:
  
  7-10 years overall with at least 5 years in advanced analytics/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:
  
  
  • Understanding statistical or numerical methods application, data mining or data-driven problem solving
  • Demonstrating thought leader level abilities in use of statistical modelling, algorithms, data mining and machine learning algorithms
  • Demonstrating proven delivery within 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 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
  • 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, Sci Py, 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 how to operationalize analytic models to run in an automated context
  • Using common cloud platforms including AWS and GCP and utilities for managing and manipulating large data sources, models, development, and deployment
  • Experience conducting research in a lab and publishing work
• Experience with following technologies:
• Programming:
  Python (must), having experience in R is a plus
• Visualization:
  Python (like Matplotlib, Seaborn, Bokeh, etc.), third party libraries (like Power BI, Tableau)
• Productionization and containerization technologies (Good to have):
  Git Hub, Flask, Docker, Kubernetes, Azure Dev Ops, GCP, Azure, AWS

• Leadership:
  • Leading initiatives aligned with growth of team and 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 resources on projects
  • Managing communication and project delivery among 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