Data Scientist - GEN AI

Job Summary

The Data Scientist - GEN AI position focuses on leveraging advanced analytics and machine learning techniques to create innovative solutions utilizing generative artificial intelligence. This role requires collaboration with cross-functional teams to deliver actionable insights and predictive models that align with organizational goals.

Responsibilities

• Develop and implement machine learning models using techniques from generative AI and deep learning.
• Utilize FastAPI for building APIs that enhance the deployment of machine learning applications.
• Apply natural language processing (NLP) techniques to extract insights from unstructured data sources.
• Collaborate with stakeholders to identify opportunities for leveraging AI and data analytics within the organization.
• Conduct exploratory data analysis to uncover patterns and trends that inform business decisions.
• Utilize Python and OpenCV for processing images and visual data relevant to projects.

Qualifications

• Educational Qualification: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
• Technical Skills:
  • Proficiency in Python, with a strong understanding of OOP principles.
  • Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch).
  • Experience in developing and deploying applications using FastAPI.
  • Knowledge of generative AI techniques and practices.
  • Strong understanding of natural language processing (NLP) methodologies.
  • Proficiency in dealing with image processing using OpenCV.
  • Familiarity with deep learning architectures.

Preferred Skills

• Experience with cloud services, particularly Microsoft Azure.
• Familiarity with additional programming languages or frameworks related to data science.
• Knowledge of big data technologies (e.g., Hadoop, Spark) could be beneficial.

Experience

• Relevant experience in data science, specifically involving machine learning and generative AI techniques. Specific years of experience are not specified.

Environment

• The typical work setting is collaborative, often involving teamwork with other data scientists and stakeholders. Work location has not been specified; it may vary between in-office, remote, or hybrid setups.