Data Scientist AI ML Solutions

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Description

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ITSAI develops platforms that create an enabling environment for leveraging AI, Machine Learning, and Search Technologies and injecting them into our digital products. We create solutions that enhance productivity, improve decision-making, and increase automation, strengthening our digital landscape. Our team develops and supports systems using traditional machine learning and generative AI models, streamlining operations and empowering business units to automate processes and make data-driven decisions.

Objective: The primary objective of this position is to design, develop, and implement machine learning models and systems that leverage knowledge graphs, generative AI, deep learning, classical machine learning models, and statistical analysis to solve complex problems and drive data-driven decision-making within the organization.

Key Responsibilities:

• Build and enhance machine learning models through all phases of development including design, training, validation, and implementation etc.
• Work on projects involving generative AI, large language models, and retrieval-augmented generation (RAG)
• Unlock insights by analyzing large scale of complex numerical and textual data and identifying trends.
• Partner with a cross-functional team of data engineers, software engineers, and data visualization to deliver projects.
• Research and evaluate emerging technologies.
• Write efficient, maintainable, and well-documented Python code.
• Develop data science solutions based on tools and cloud computing infrastructure.

Selection Criteria:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field with 2 years relevant experience in machine learning or AI development OR equivalent combination of education and experience. 
• Demonstrated track record of successful ML/AI project implementations.

Required Skills and Expertise:

• In-depth knowledge of large language models (LLMs).
• Experience with retrieval-augmented generation (RAG) techniques.
• Require a good understanding of deep learning architectures and algorithms.
• Proven experience in machine learning, deep learning, and statistical analysis.
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and Keras.
• Strong understanding of regression models and ensemble methods.
• Ability to select and apply appropriate ML algorithms for given problems.
• Expertise in statistical analysis and hypothesis testing.
• Skills in exploratory data analysis and data visualization.

Source: https://worldbankgroup.csod.com/ats/careersite/JobDetails.aspx?id=29302&site=1

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