335143ResponsibilitiesApply data science and statistical methods to solve complex engineering problems and enhance simulation, design, and manufacturing workflows.Develop Python-based tools for simulation automation, pre/post-processing, and reporting.Design and implement dashboards and data visualization tools to support decision-making.Implement intelligent algorithms for pattern recognition and anomaly detection.Design and execute DoEs for both virtual simulations and physical testing scenarios.Leverage LLMs, autonomous agents, and generative AI tools in engineering context.Support digital transformation initiatives through AI/ML integration.Collaborate with cross-functional teams (design, manufacturing, testing).Mentor junior engineers and contribute to technical presentations and knowledge sharing. Drive continuous improvement in engineering processes.

2. Preferred Experience

B.Tech or master’s in mechanical engineering, data science, computer science or related field.Strong understanding of FEA and knowledge of comprehensive structural, thermal, dynamic, and multi-physics simulations. Knowledge on CAE tools like ANSYS, LS-DYNA Altair Hyperworks, with an understanding of their application in nonlinear plastics simulations.Strong foundation in applied statistics, data analytics, and both supervised and unsupervised learning techniques.Foundation in engineering mechanics, materials science, and thermodynamics, knowledge of FEA tools (Ansys/LS-Dyna/Altair) is a strong asset.5 years of experience in data science and CAE , preferably in the automotive industry. Proficiency in Python and experience with ML libraries. Experience with cloud platforms (Azure, AWS) containerization (Docker) and web frameworks (Flask, Django).Familiarity with DevOps practices, version control systems (Git), MLOps environments (e.g Azure ML).Experience with data visualization tools (e.g. Matplotlib, Plotly, PyVista)Experience with building and scaling analytics solutions on cloud-based platformsExperience working with real-world sensor data, knowledge of signal processing techniquesKnowledge of prompt engineering and deployment of GenAI agentsFluency in English; willingness to travel occasionally (domestic and international).

3. Competencies

Strong analytical and problem-solving skills.Ability to handle complex, multidisciplinary engineering challenges.Innovative mindset for developing creative solutions.Clear communication of technical concepts to diverse audiences.Strong presentation and mentoring skills.Passion for continuous learning and technology adoption.Flexibility in dynamic project environments.Proactive in process improvement and innovation.Ability to bridge simulation, testing, and AI workflowsStrong understanding of validation strategies and test-to-simulation alignmentResponsibilitiesApply data science and statistical methods to solve complex engineering problems and enhance simulation, design, and manufacturing workflows.Develop Python-based tools for simulation automation, pre/post-processing, and reporting.Design and implement dashboards and data visualization tools to support decision-making.Implement intelligent algorithms for pattern recognition and anomaly detection.Design and execute DoEs for both virtual simulations and physical testing scenarios.Leverage LLMs, autonomous agents, and generative AI tools in engineering context.Support digital transformation initiatives through AI/ML integration.Collaborate with cross-functional teams (design, manufacturing, testing).Mentor junior engineers and contribute to technical presentations and knowledge sharing. Drive continuous improvement in engineering processes.

2. Preferred Experience

B.Tech or master’s in mechanical engineering, data science, computer science or related field.Strong understanding of FEA and knowledge of comprehensive structural, thermal, dynamic, and multi-physics simulations. Knowledge on CAE tools like ANSYS, LS-DYNA Altair Hyperworks, with an understanding of their application in nonlinear plastics simulations.Strong foundation in applied statistics, data analytics, and both supervised and unsupervised learning techniques.Foundation in engineering mechanics, materials science, and thermodynamics, knowledge of FEA tools (Ansys/LS-Dyna/Altair) is a strong asset.5 years of experience in data science and CAE , preferably in the automotive industry. Proficiency in Python and experience with ML libraries. Experience with cloud platforms (Azure, AWS) containerization (Docker) and web frameworks (Flask, Django).Familiarity with DevOps practices, version control systems (Git), MLOps environments (e.g Azure ML).Experience with data visualization tools (e.g. Matplotlib, Plotly, PyVista)Experience with building and scaling analytics solutions on cloud-based platformsExperience working with real-world sensor data, knowledge of signal processing techniquesKnowledge of prompt engineering and deployment of GenAI agentsFluency in English; willingness to travel occasionally (domestic and international).

3. Competencies

Strong analytical and problem-solving skills.Ability to handle complex, multidisciplinary engineering challenges.Innovative mindset for developing creative solutions.Clear communication of technical concepts to diverse audiences.Strong presentation and mentoring skills.Passion for continuous learning and technology adoption.Flexibility in dynamic project environments.Proactive in process improvement and innovation.Ability to bridge simulation, testing, and AI workflowsStrong understanding of validation strategies and test-to-simulation alignment

B.Tech or master’s in mechanical engineering, data science, computer science or related field.