Data Science Engineer

2 weeks ago

Bengaluru, Karnataka, India MyCareernet Full time ₹ 9,00,000 - ₹ 12,00,000 per year

Company:Indian / Global Engineering & Manufacturing Organization

Key Skills:
Data Science, CAE - Powertrain, Python, LLM, Simulation

Roles & Responsibilities:

  • Apply advanced data science, statistical, and machine learning techniques to address complex engineering and design challenges.
  • Develop and maintain Python-based tools for simulation automation, pre/post-processing, and performance reporting.
  • Build intelligent dashboards and visualization systems to enable data-driven decision-making in engineering processes.
  • Implement predictive modeling, anomaly detection, and optimization algorithms to enhance simulation accuracy and reliability.
  • Design and conduct Design of Experiments (DoEs) for both virtual and physical test scenarios to optimize performance parameters.
  • Integrate LLMs (Large Language Models), autonomous agents, and generative AI tools into CAE and engineering workflows.
  • Collaborate closely with cross-functional teams in design, manufacturing, and testing to enable data-centric engineering solutions.
  • Contribute to digital transformation initiatives by embedding AI/ML frameworks into simulation and validation environments.
  • Document methodologies, maintain data pipelines, and ensure version control for reproducibility and traceability.
  • Mentor junior engineers and data scientists, fostering technical excellence and continuous learning.
  • Support innovation and process improvement efforts by identifying opportunities for automation and efficiency gains.

Experience Required:

  • 5 - 6 years of hands-on experience in data science and CAE-related applications.
  • Proven expertise in Python programming, data analysis, and automation for engineering simulations.
  • Experience working with CAE tools (e.g., ANSYS, GT-Power, Simulink) and integrating them with Python or ML workflows.
  • Strong understanding of powertrain systems, mechanical simulations, and physical modeling.
  • Exposure to LLMs, generative AI, and autonomous systems applied in engineering contexts.
  • Practical experience in deploying ML models, building APIs, and using cloud-based data platforms.
  • Ability to manage data pipelines, clean large datasets, and create interpretable models.
  • Demonstrated capability in applying AI/ML methods for predictive maintenance, optimization, or anomaly detection.
  • Excellent problem-solving skills and ability to communicate insights to both technical and non-technical stakeholders.
  • Strong collaboration and project management skills in cross-functional engineering teams.

Education:
Any Graduation.

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