Data Scientist - AI

Key Responsibilities:

1. Design, develop, and maintain scalable, efficient, and reliable systems to support GenAI and machine learning-based applications and use cases
2. Lead the development of data pipelines, architectures, and tools to support data-intensive projects, ensuring high performance, security, and compliance
3. Collaborate with other stakeholders to integrate AI and ML models into production-ready systems
4. 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
5. Ensure high-quality code, following best practices, and adhering to industry standards and company guidelines

Hard Requirements:

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

Mandatory Skills:

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