Senior Data Scientist II

Responsibilities: 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.