Data Science Analyst

Discover your future at Citi

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Job Overview

The Spec Analytics Intmd Analyst is a developing professional role. Deals with most problems independently and has some latitude to solve complex problems. Integrates in-depth specialty area knowledge with a solid understanding of industry standards and practices. Good understanding of how the team and area integrate with others in accomplishing the objectives of the sub function/ job family. Applies analytical thinking and knowledge of data analysis tools and methodologies. Requires attention to detail when making judgments and recommendations based on the analysis of factual information. Typically deals with variable issues with potentially broader business impact. Applies professional judgment when interpreting data and results. Breaks down information in a systematic and communicable manner. Developed communication and diplomacy skills are required in order to exchange potentially complex/sensitive information. Moderate but direct impact through close contact with the businesses' core activities. Quality and timeliness of service provided will affect the effectiveness of own team and other closely related teams.

Key Responsibilities –

• Model Development: Assist in the development, fine-tuning, and deployment of generative AI models and large language models.
• Data Preparation: Clean, preprocess, and organize large datasets for training and evaluation purposes.
• RAG Frameworks – Customize and fine-tune existing / new RAG frameworks to meet project requirements.
• Research and Experimentation: Conduct experiments to test and validate model performance and keep up to date with the latest advancements in the field of AI and NLP.
• Performance Optimization: Implement techniques to improve model efficiency, accuracy, and scalability.
• Collaborative Projects:  Work with cross-functional teams to integrate AI models into various applications and services.
• Documentation: Maintain clear and comprehensive documentation of model architectures, processes, and findings

Qualifications –

• Bachelor’s degree in computer science, Data Science, Electrical Engineering, or a related field. A master's degree is a plus.
• Programming Skills: Proficiency in programming languages such as Python, and familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
• Mathematics and Statistics: Strong understanding of fundamental concepts in mathematics and statistics, including linear algebra, calculus, and probability.
• NLP Knowledge: Basic knowledge of natural language processing techniques and concepts, such as tokenization, embeddings, and sequence models.
• Problem-Solving Skills: Ability to analyze complex problems, develop innovative solutions, and effectively communicate findings.
• Teamwork: Strong collaborative skills and a willingness to work in a dynamic team environment.
• Curiosity and Learning: Eagerness to learn and stay current with the latest research and trends in generative AI and LLMs.

Preferred Skills –

• 5+ years of relevant experience in data science, machine learning, or a related field.
• Previous experience with AI/ML projects, internships, or relevant coursework.
• Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and version control systems (e.g., Git).
• Experience with deploying machine learning models in a production environment.
• Strong communication skills, both verbal and written, and the ability to explain technical concepts to non-technical stakeholders. 

Education:

• Masters/University degree or equivalent experience

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Job Family Group:

Decision Management

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Job Family:

Specialized Analytics (Data Science/Computational Statistics)

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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