Data Scientist - AI

Area(s) of responsibility

Job Description

Key Responsibilities:

• Design, develop, and maintain scalable, efficient, and reliable systems to support GenAI and machine learning-based applications and use cases
• Lead the development of data pipelines, architectures, and tools to support data-intensive projects, ensuring high performance, security, and compliance
• Collaborate with other stakeholders to integrate AI and ML models into production-ready systems
• Work closely with non-backend expert counterparts, such as data scientists and ML engineers, to ensure seamless integration of AI and ML models into backend systems
• Ensure high-quality code, following best practices, and adhering to industry standards and company guidelines

Hard Requirements:

• Senior backend engineer with a proven track record of owning the backend portion of projects
• Experience collaborating with product, project, and domain team members
• Strong understanding of data pipelines, architectures, and tools
• Proficiency in Python (ability to read, write and debug Python code with minimal guidance)

Mandatory Skills:

• Machine Learning: experience with machine learning frameworks, such as scikit-learn, TensorFlow, or PyTorch
• Python: proficiency in Python programming, with experience working with libraries and frameworks, such as NumPy, pandas, and Flask
• Natural Language Processing: experience with NLP techniques, such as text processing, sentiment analysis, and topic modeling
• Deep Learning: experience with deep learning frameworks, such as TensorFlow, or PyTorch
• Data Science: experience working with data science tools
• Backend: experience with backend development, including design, development, and deployment of scalable and modular systems
• Artificial Intelligence: experience with AI concepts, including computer vision, robotics, and expert systems
• Pattern Recognition: experience with pattern recognition techniques, such as clustering, classification, and regression
• Statistical Modeling: experience with statistical modeling, including hypothesis testing, confidence intervals, and regression analysis