Senior Data Scientist
Key Responsibilities:
- Design, develop, and optimize recommendation systems to enhance user experience and engagement across platforms.
- Build and deploy chatbots with advanced NLP capabilities for automating customer interactions and improving business processes.
- Lead the development of Generative AI solutions, including content generation and automation.
- Research and apply Large Language Models (LLMs) like GPT, BERT, and others to solve business-specific problems and create innovative solutions.
- Collaborate with engineering teams to integrate machine learning models into production systems, ensuring scalability and reliability.
- Perform data exploration, analysis, and feature engineering to improve model performance.
- Stay updated on the latest advancements in AI and ML technologies, proposing new techniques and tools to enhance our product capabilities.
- Mentor junior data scientists and engineers, providing guidance on best practices in AI/ML model development and deployment.
- Collaborate with product managers and business stakeholders to translate business goals into AI-driven solutions.
- Work on model interpretability, explainability, and ensure models are built in an ethical and responsible manner.
Required Skills And Qualifications:
- 5+ years of experience in data science or machine learning, with a focus on building and deploying AI models.
- Strong expertise in designing and developing recommendation systems and working with collaborative filtering, matrix factorization, and content-based filtering techniques.
- Hands-on experience with chatbots using Natural Language Processing (NLP) and conversational AI frameworks.
- In-depth understanding of Generative AI, including transformer-based models and GANs (Generative Adversarial Networks).
- Experience working with Large Language Models (LLMs) such as GPT, BERT, T5, etc.
- Proficiency in machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Strong programming skills in Python and libraries such as NumPy, Pandas, Hugging Face, and NLTK.
- Experience with cloud platforms like AWS, GCP, or Azure for deploying and scaling machine learning models.
- Solid understanding of data pipelines, ETL processes, and working with large datasets using SQL or NoSQL databases.
- Knowledge of MLOps and experience deploying models in production environments.
- Strong problem-solving skills and a deep understanding of statistical methods and algorithms.
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