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Company Overview
This organization is a prominent player in the aviation industry, known for its commitment to innovation and leveraging cutting-edge technologies to enhance its operations and customer experiences. They are building a strong in-house AI and data science capability to drive impactful solutions across various aspects of their business.
Job Title: Information Technology – Data Scientist (Data Science Track)
Key Responsibilities include:
They will be a member of an in-house AI and data science development team that works on AI (including areas in generative AI, autonomous agent, NLP, computer vision and recommender system), mathematical optimization, game theory, and experimental design.
The individual will work closely with business stakeholders to create impactful and intelligent features/services in AI, data science and data analytics. They will propose and build scalable ML/DL solutions and deploy them as API microservices for use by software applications and business users for faster and more effective decision making.
They will oversee the technical work of external technology partners and provide them datasets to deliver products/services in AI and data science. The role involves supporting business users in the assessment/ validation of partner-supplied prediction models and in their deployment to production cloud.
The candidate will work closely with application development teams to operationalize and integrate AI/ML capabilities in API microservices.
Note: The candidate could be posted to any subsidiary within the group.
Requirements
A BS in Computer Science, Mathematics, Statistics, Physics or a related discipline is required. PhD and MS degrees related to machine learning and other AI disciplines are preferred.
Intermediate programming skills in Python are necessary, along with being conversant with algorithm design/analysis, data structure, and SQL. Familiarity with functional/object-oriented software development using modern programming languages such as Scala, TypeScript/JavaScript, Java, and C# is a plus.
Exposure to the use of workflow/map-reduce and stream processing systems such as Spark and Kafka for big-data processing is expected.
Relevant internship or industry experience as a hands-on data scientist/AI engineer in shallow and/or deep learning is needed (comfort with most topics covered in the free undergraduate textbook by Gareth James, “An Introduction to Statistical Learning – with Applications in Python”, 2023 is important). Additional exposure in some of the following areas is a plus: Use of recent proprietary/open-source LLMs through prompt engineering (such as OpenAI ChatGPT and Bing Chat), familiarity with LLM application/data frameworks (such as LlamaIndex and LangChain).
Experience using GPU-accelerated deep learning frameworks (such as PyTorch and TensorFlow) is beneficial.
Familiarity with Bayesian statistics/inference and Bayesian/causal networks for probabilistic reasoning is a plus.
Some hands-on experience with AWS, Azure, GCP, or other public cloud environments is desired.
Good interpersonal and communication skills for working with both technical staff and non-technical business users are required.
Experience with Agile/Scrum/Kanban methodologies is a plus.
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