Simulation Manager - Big4 Accounting Firm

Basic Qualifications:

Level : Associate 2

Minimum Year(s) of Experience: 2-4 (At Least 2 year(s) experience in simulation model development / Machine learning Operations)

Level of Education/ Specific Schools: Graduate/Post graduate from reputed institute(s) with relevant experience.

Field of Experience/ Specific Degree: BTech, MTech, Masters in Statistics/ Mathematics

Knowledge Required:

- Experience with a subset in each of the following technologies:

Software: AnyLogic, STELLA, Arena

Programming: Python, Java

Data Processing Tools: Python (Numpy, Pandas, etc.), Spark, cloud-based solutions such as GCP DataFlow;

Machine Learning Libraries: Python (scikit-learn, pysim, etc.), TensorFlow, Keras, PyTorch, Spark MLlib;

Code collaboration: git, github

- Independently working on building simulation models using object oriented approaches. Experience with agent based modeling, system dynamics modeling, discrete event modeling

- Experience with Systems Thinking concepts and application is a big plus.

- Demonstrated ability to create end-to-end technology prototypes and/or machine learning models for a given business use case or application.

- Demonstrated experience with rapid prototyping, using agile approaches to quickly test new ideas and "fail fast".

- Demonstrated ability to apply a business framing to emerging technology solutions and communicate to business audiences in written and verbal formats.

- Demonstrated interest in emerging technologies such as Artificial Intelligence, Blockchain, Internet of Things, Virtual Reality, Augmented Reality, and Robotics.

- Experience in innovation or lab environments is a plus.

- Excellent communication skills

Role and Responsibilities:

- Quickly explore new analytical technologies related to simulation and evaluate their technical and commercial viability.

- Work in sprint cycles to develop proof-of-concepts and prototypes that can be demoed and explained to data scientists, internal stakeholders, and clients.

- Quickly test and reject hypotheses around data processing and machine learning/simulation modeling.

- Experiment, fail quickly, and recognize when you need assistance vs. when you conclude that a technology is not suitable for the task.

- Develop, deploy and manage production pipeline of ML models; automate the deployment pipeline

- Stay abreast of new AI and simulation research from leading labs by reading papers and experimenting with techniques.

- Develop innovative solutions and perspectives on AI and simulation that can be published in academic journals/arXiv and shared with clients.