Principal Data Scientist

Job Description

Job Summary:

As the Principal Data Scientist within Lab 37, you will lead the strategic vision and execution of cutting-edge AI/ML initiatives. Your role will be pivotal in shaping the direction of Lab 37, driving transformative change within the organization, and furthering Indigo's leadership in the dynamic aviation industry.

Job Responsibilities:

• Provide visionary leadership and strategic direction to Lab 37, overseeing the development and implementation of innovative AI software solutions and applications.
• Lead the translation of state-of-the-art research into scalable and robust AI software, ensuring seamless integration into existing digital systems and processes.
• Drive the enhancement of AI/Machine Learning models through advanced experimentation and analysis, closely aligning them with business objectives and requirements.
• Collaborate closely with cross-functional teams within the Digital function and across the organization to identify complex challenges and develop effective solutions that drive operational efficiency and business growth.
• Foster a culture of collaboration, innovation, and excellence within Lab 37, nurturing the professional development and growth of team members.
• Serve as a mentor and thought leader, providing guidance and expertise to junior team members, including freshers and interns.

Qualification Requirements:

Educational:

• B.Tech / M.Tech in Computer Science, Data Science, Machine Learning, or a related field, with a minimum of 15+ years of relevant experience.
• Demonstrated expertise in deep learning techniques through academic research or practical application.

Technical:

• Extensive experience leading AI/ML projects in an industrial setting, with a focus on Python programming and deep learning frameworks such as TensorFlow and PyTorch.
• Proven track record of building and deploying production-level deep learning models, including Feed Forward neural networks, CNNs, LSTMs, GANs, and Transformers.
• Proficiency in deploying optimized ML models using frameworks such as tensorrt, ONNX, or Triton Inference Server, with efficient APIs.
• Strong background in developing predictive models on large tabular data sets or solving complex ML problems in NLP or CV domains.
• Familiarity with Snowflake, Databricks, and expertise in container technologies (Kubernetes, Docker) is preferred.
• Proficiency in at least one cloud platform (AWS, Azure, GCP) is required.
• Exceptional attention to detail with the ability to produce accurate engineering documentation.

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