Manager - Machine Learning - BFS/IT

Machine Learning Manager

Basic Qualifications :

- Level : Manager- strong in ML, DL,NLP,AI ,Python or other language

- 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/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