Senior Data Scientist

Key Responsibilities:

• Design, develop, and deploy scalable AI, Machine Learning, Generative AI, and Agentic AI solutions, including intelligent assistants, copilots, conversational systems, and enterprise-grade AI products that solve complex business problems and deliver measurable value.

• Translate business requirements into scalable AI architectures and technical solutions by defining solution designs, integration strategies, technical specifications, data flows, and implementation approaches while ensuring alignment with security, governance, reliability, maintainability, and responsible AI standards.

• Develop, evaluate, and optimize AI and machine learning models and platforms, establishing testing frameworks, performance metrics, monitoring, observability, and production-readiness practices to improve accuracy, scalability, efficiency, and business impact.

• Lead end-to-end delivery of AI initiatives by driving solution development, deployment, operationalization, troubleshooting, continuous improvement, enterprise integrations, and collaboration with engineering teams to build scalable data pipelines and AI workflows.

• Partner with Product Managers, Engineering teams, and business stakeholders to define product capabilities, technical requirements, roadmaps, and best practices while providing technical leadership, influencing architectural decisions, and effectively communicating complex concepts to technical and non-technical audiences.

Required Qualifications:

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related field.

• 8+ years of experience in Data Science, Machine Learning, Artificial Intelligence, or software development.

• Demonstrated experience delivering production-grade AI or machine learning solutions.

• Strong experience with Generative AI, Large Language Models, and modern AI frameworks.

• Experience designing scalable enterprise applications and distributed systems.

• Proven track record of independently owning and delivering complex technical initiatives.

• Strong stakeholder management and cross-functional collaboration skills.

Technical Skills:

• Advanced expertise in Artificial Intelligence, Data Science, Machine Learning, and Generative AI, including Agentic AI, Large Language Models (LLMs), Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), Prompt Engineering, Statistical Modeling, Predictive Analytics, Quantitative Analysis, Data Mining, Data Analysis, and Applied AI solution development.

• Strong hands-on experience with modern AI and data science frameworks and tools, including LangChain, LangGraph, Azure OpenAI, NoSQL, Azure Machine Learning, TensorFlow, MLflow, LlamaFactory, Unsloth, Scikit-Learn, Pandas, NumPy, Matplotlib, SpaCy, GPT models, Embeddings, and Vector Search technologies.

• Proficiency in Python, SQL, API development, FastAPI, Flask, distributed systems, cloud-native application development, CI/CD practices, Git, Jupyter, and modern software engineering practices for production-grade AI solutions.

• Experience designing, developing, and deploying enterprise-scale solutions on Azure, Azure AI Services, Azure Machine Learning, Azure OpenAI, Cosmos DB/Dynamo DB, cloud-based AI platforms, and containerized environments.

• Strong understanding of data engineering and analytics, including data pipelines, data cleansing, wrangling, visualization, feature engineering, experiment design, model evaluation, data modelling, analytics, business insights, and AI performance measurement methodologies.

• Expertise in building scalable Conversational AI, Intelligent Agents, Multi-Agent Systems, Knowledge Retrieval platforms, AI orchestration frameworks, enterprise integrations, and production AI systems, including fine-tuning, reasoning, document understanding, summarization, classification, and question-answering solutions.

• Experience applying advanced machine learning techniques including classification, clustering, recommendation systems, forecasting, ensemble learning, hyperparameter tuning, feature selection, embedding-based modelling, similarity search, and model optimization.

Success Measures

Success in this role is measured through:

• Delivery of scalable AI and Data Science solutions that create measurable business impact.

• Successful implementation of Generative AI and advanced analytics capabilities.

• High-quality technical designs and architectural contributions.

• Improvement of platform performance, reliability, and user experience.

• Effective collaboration across product, engineering, and business teams.

• Technical leadership and influence across AI initiatives.

• Consistent ownership and successful completion of complex projects and workstreams.

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.