Senior Data Scientist II

• Collect data, perform data analysis, develop models, define quality metrics, and conduct quality assessments of models, along with regular presentations to stakeholders.
• Create production-ready Python packages for each component of data science pipelines (e.g., pre-processing, model inference, evaluation) and coordinate their deployment with the technology team.
• Design, develop, and deploy Generative AI models and solutions that meet specific business needs.
• Expertise in Retrieval Augmented Generation (RAG) optimization and customization of existing RAG pipelines to meet specific project needs.
• Proficiency in large-scale data ingestion, preprocessing, and transformation of multilingual content to ensure high-quality inputs for downstream models.
• Experience building Agentic RAG systems is strong requirements.
• Experience in LangChain, AutoGen, Haystack, MCP or similar AI agent management tools.
• Fine-tune large language models (LLMs) and transformer models to enhance accuracy and relevance.
• Implement guardrails and evaluation mechanisms to ensure responsible and ethical AI usage.
• Conduct rigorous testing and evaluation of AI models to ensure high performance and reliability.
• Integrate data science components and ensure end-to-end quality assessment.
• Maintain the robustness of data science pipelines against model drift and ensure consistent output quality.
• Establish a reporting process for pipeline performance and develop automatic re-training strategies for existing pipelines.
• Work collaboratively with cross-functional teams to integrate AI solutions into existing products and services.
• Mentor junior data scientists and contribute to the knowledge-sharing culture within the team.
• Stay up-to-date with the latest advancements in AI, machine learning, and NLP technologies.

Requirements

• Master s or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related field.
• 7+ years of relevant applied experience in data science, with a focus on Generative AI, NLP, and machine learning.
• Proficiency in Python for data analysis, model development, and deployment.
• Strong experience with transformer models and fine-tuning techniques for large language models (LLMs).
• Proficiency in Generative AI technologies, including utilizing LLMs via API access, LLM evaluation tools, and prompt engineering.
• Knowledge of various RAG pipelines and their practical implementation.
• Experience with advanced algorithms in deep learning, neural networks, reinforcement learning, and transfer learning.
• Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing.
• Understanding of AI ethics, guardrail implementation, and evaluation metrics.
• Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment and the creation of production-ready pipelines.
• Proficiency in data visualization tools and techniques.
• Experience with version control systems (e.g., GitLab or GitHub), Jira, and working in an Agile environment.
• Proficient in using *nix systems, open-source software, Jupyter Notebook, libraries, and cloud computing.
• Excellent problem-solving and analytical skills, with strong attention to detail.
• Strong communication skills and the ability to work effectively in a team-oriented environment.