Data Scientist

Role & responsibilities

• Design, develop, and deploy AI/ML models addressing real-world aerospace and industrial challenges.
• Build and fine-tune ML & DL models including Supervised, Unsupervised Learning, CNNs, RNNs, Transformers, and domain-specific LLMs.
• Apply Machine Learning & Deep Learning for simulation, modeling, and prediction tasks.
• Develop and contribute to Generative AI solutions, including prompt engineering and RAG system design.
• Analyze multi-modal datasets, extract insights, and translate findings into actionable solutions.
• Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
• Perform exploratory and targeted data analysis using descriptive statistics and other methods
• Prepare technical documentation and present outcomes to technical and non-technical stakeholders.
• Working knowledge of LLMOps
• Experience working with diverse and complex datasets, including time-series sensor data, simulation outputs, engineering text/diagrams, or geospatial data, is a significant advantage

Preferred candidate profile

• Bachelors or above in Computer Science, Data Science, Machine Learning, Applied Mathematics, or related fields.
• Solid understanding of machine learning, deep learning, and statistical modeling.
• Hands-on experience with frameworks such as PyTorch, Hugging Face Transformers, or similar.
• Experience with Generative AI concepts (e.g., prompt engineering, fine-tuning LLMs, building RAG systems) is a plus.
• Strong coding skills in Python and libraries such as NumPy, Pandas, Scikit-learn & PyTorch
• Exposure to MLOps practices (versioning, reproducibility, containerization) is an advantage.
• Creative, self-motivated, and able to work collaboratively in a multi-disciplinary innovation environment.
• Good communication skills and a passion for solving real-world problems using AI.