Job Description

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

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