Senior Research Scientist/Lead Scientist - First Principles Modelling

Responsibilities:

1. Responsible for developing new features and functionality for the various mechanical and chemical processes.

2. Primary responsibility includes mathematical model formulation, implementation on simulation software. This includes but is not limited to developing/maintaining library of process unit operations in steady state, transient & simulation utilities on the tools.

3. Responsible for delivering top notch quality product.

4. Be part of entire product development life cycle: idea inception, application/feature design, implementation adhering to coding standards, test, and address customer feedback.

5. Responsible for interfacing with relevant Internal and external stakeholders throughout development life cycle.

Required Qualifications:

Essential Skills :

1. Bachelors and master’s in mechanical engineering from premium institute/university with 8-15 years of prior work experience in developing system simulation

2. Strong fundamentals in Mechanical Engg - Physics first principles modeling.

3. Hands on mathematical formulation of unit operations, Numerical methods for solving these equations and flow sheet wide sequential solver approaches.

4. Ability to simplify modelling equations for better model performance.

5. Exposure to constrained optimization or non-linear solvers.

6. Knowledge and Experience in HVAC System, Utility System like Boilers and Turbine, Heat Exchanger, Pumps, Fans, Compressor, Conveying systems.

7. Hands on with 1D simulation tools like GT Suite, AmeSim, MATLAB/Simulink, Dymola.

Preferred Skills :

1. Hands on with 3D simulation tools like StarCCM, ANSYS Fluent.

2. Team player, ability to augment skills through self-learning and sharing.

3. Deep customer empathy and customer-first mind set. Qualitatively relate mathematical model to real time process behavioral pattern.

4. Should be proficient in object-oriented programming preferably in C++, Python

5. Experience in Machine Learning/Deep Learning models. Experience in statistical time-series analytics and forecasting: quartiles, ETS forecast, Holt-Winters, auto regressive techniques, XGBoost, ANN, ARIMA/SARIMA, GARCH, hybrid GARCH-LSTM.

Additional Details

• Experience: 8-15?years
• Employement Type: Full Time