Manager/Senior Manager - Deep Learning/Machine Learning

Job Description

Manager/Senior Manager - Deep Learning/Machine Learning/Artificial Intelligence - KPO ( Female Hiring )Level : Manager- Minimum Year(s) of Experience: 7- 10 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/ScienceKnowledge 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 plusExperience 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,

Keyskills :
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