Data Science Researcher

Life @ESPL

At ESPL, we believe continuous learning is key to staying ahead in the ever-evolving world of technology. Being a growing mid-sized organization, we offer opportunities for our employees at every level of their expertise and journey with us. We ensure our employees grow systematically through our training programs that foster that growth.

Unity in diversity: Young and dynamic professionals with passion for engineering form the ESPL team. With focus on innovation and growth, ESPL family as a whole has strived through different challenges. We appreciate our team for their contribution and dedication.

Technical competence is basic requirement of CAE domain and we ensure that our team is well acquainted with latest technical trends in the business. We have developed comprehensive training programs for our team for developing Technical know- how, Soft skills, Leadership development and Data security awareness.

Work Culture

We support multi-continental customers. It is always interesting to know different work cultures from different regions and we like to implement the best from each in our team. Professionalism and commitment are the key values at the core of ESPL team.

We ensure development of our employee’s professional career by providing long term opportunities to work on challenging tasks, developing expertise required for the execution and maintaining work- life balance in a supportive and approachable manner.

Various team bonding activities ensures that at the end we stand strong together as one unit. Knowledge sharing and communication plays a vital role in development of each and every person individually as well as in a team.

Why Work with ESPL

We are committed to delivering comprehensive virtual engineering solutions from concept to prototype. Our team of skilled engineers resonates with this objective and stays at the core of every project. Here’s what makes ESPL a great place for engineers.

Advanced Engineering Challenges

Addressing and resolving engineering challenges is one of the factors defining our existence. With us, you will have the opportunity to solve complex problems in fields like Model-Based System Engineering (MBSE), AI-driven Data Engineering, and E-Powertrain development for global automotive brands. Are you game for it?

Professional Development

We prioritize continuous learning to keep our engineers at the forefront of technology and innovation. That’s one reason our engineers consistently deliver competent solutions.

Collaborative Culture

At ESPL, we value collaborations and nurture a culture that drives inter-team collaborations. We boast a team-oriented workplace where you get the opportunity to work with top and seasoned engineers.

Please send your profile at [email protected]

Data Science Researcher

Key Responsibilities

• Develop and implement machine learning algorithms, including neural networks, to optimize mechanical engineering processes and predict system behaviors.
• Use Python programming and libraries (such as TensorFlow, PyTorch, SciPy, pandas) to design and test machine learning models for Finite Element / Finite Volume simulations.
• Collaborate with mechanical engineering teams to integrate data-driven solutions into product designs and simulations.
• Analyze large datasets from simulations, experiments, and real-world systems to derive meaningful insights for design optimization and predictive maintenance.
• Build and fine-tune predictive models to improve the accuracy and performance of engineering products.
• Conduct data pre-processing, feature engineering, and model validation to ensure high-quality results.
• Prepare reports and visualizations to communicate complex findings to technical and non-technical stakeholders.
• Stay updated with the latest advancements in machine learning and data science, particularly as they apply to mechanical engineering.

Qualifications

• Experience: 3-5 years of experience in data science or machine learning, with a strong background in mechanical engineering or a related field.
• Education: Master’s degree in Mechanical Engineering, Data Science, Applied Mathematics, or a related discipline. PhD Candidates are welcomed.
• Technical Skills:
  • Proficiency in Python programming and machine learning libraries such as TensorFlow, Keras, PyTorch, and Scikit-learn.
  • Strong understanding of neural networks, deep learning, and machine learning algorithms.
  • Knowledge of mechanical engineering concepts, such as structural mechanics, thermodynamics, or fluid dynamics.
  • Familiarity with data manipulation and visualization tools (e.g., pandas, NumPy, matplotlib, seaborn).