Overview

Nextlink is looking for a hands-on GenAI Data Scientist to join the Data Analytics & Foundational Platforms team within Wealth Management Americas.

Responsibilities

• Design, develop, test, and deploy Generative AI and machine learning solutions in collaboration with senior data scientists and technical leads.
• Build AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, embeddings, and agent-based architectures.
• Develop and maintain evaluation frameworks to assess the accuracy, reliability, consistency, and performance of LLM-based applications.
• Prepare, process, and analyze structured and unstructured data for machine learning and AI application development.
• Experiment with prompt engineering, retrieval strategies, model configurations, embeddings, and fine-tuning techniques to optimize solution performance.
• Develop, validate, and implement machine learning models using techniques such as classification, clustering, and optimization.
• Write clean, reusable, maintainable Python code following software engineering, version control, testing, and reproducibility best practices.
• Develop Power BI dashboards and visualizations to communicate analytical insights and support business decision-making.
• Work with SQL databases to extract, transform, validate, and prepare data for analytical and machine learning use cases.
• Monitor production AI applications, investigate issues, perform root-cause analysis, and continuously improve solution quality and performance.
• Collaborate with business stakeholders, product managers, data scientists, and technology teams to translate business requirements into effective technical solutions.
• Document methodologies, experiments, model performance, technical decisions, and implementation outcomes.
• Ensure responsible AI practices, output validation, compliance with organizational policies, and adherence to applicable risk and governance standards.

Qualifications

• Strong hands-on programming experience in Python , including familiarity with commonly used data science and machine learning libraries.
• Practical experience developing or prototyping Generative AI applications using LLMs.
• Understanding of Natural Language Processing (NLP), deep learning, machine learning, and agentic AI systems.
• Hands-on knowledge of RAG pipelines, prompt engineering, embeddings, vector search, semantic retrieval, and information retrieval systems.
• Experience evaluating machine learning models or LLM-based applications using appropriate performance and quality metrics.
• Strong SQL skills and experience working with structured data, databases, and analytical data preparation.
• Mandatory hands-on experience in Power BI , including dashboard development and data visualization.
• Familiarity with machine learning techniques such as classification, clustering, and optimization.
• Exposure to cloud and data platforms such as Microsoft Azure, AWS, GCP, or Databricks.
• Familiarity with Git, version control, code reviews, automated testing, and software development best practices.
• Strong analytical, problem-solving, communication, and stakeholder-management skills.
• Ability to work collaboratively in a fast-paced environment and deliver practical, business-focused solutions.
• Bachelor's or master's degree in Computer Science, Data Science, Machine Learning, Statistics, Computational Linguistics, or a related quantitative discipline.
• GenAI and LLMs: Azure OpenAI, Anthropic APIs, OpenAI SDKs.
• AI/ML frameworks: PyTorch, Hugging Face Transformers, spaCy.
• LLM orchestration: LangChain, LangGraph, or equivalent frameworks.
• LLM evaluation: DeepEval or comparable evaluation frameworks.
• Data and analytics platforms: PostgreSQL, Greenplum, Hadoop, Databricks, or similar technologies.
• Business intelligence: Power BI and Tableau.
• Prior experience in financial services, banking, wealth management, or investment management.
• Experience developing enterprise search, question-answering, document intelligence, or AI-assisted workflow automation solutions.
• Exposure to productionizing and monitoring AI/ML applications in cloud environments.