Data Scientist

Overview

As a Data Scientist II, you will contribute to the design, development, testing, and delivery of data science, Machine Learning, Generative AI, and Agentic AI solutions that address business needs and improve customer experiences. This is an individual contributor role working with moderate guidance.

What you'll do

• Prepare and analyze structured and unstructured data, engineer features, and contribute to predictive models, NLP and LLM solutions, RAG pipelines, intelligent assistants, and Agentic AI workflows.
• Apply suitable metrics, test datasets, error analysis, experiment tracking, model validation, performance testing, and responsible AI checks to improve accuracy, relevance, reliability, latency, and cost.
• Build reusable code, APIs, notebooks, pipelines, containers, model artifacts, monitoring components, and automated workflows with engineering and senior data science team members.
• Translate requirements into assigned deliverables and communicate insights through visualizations, prototypes, reports, and documentation.
• Manage code, data, and experiments in line with security, privacy, governance, version control, responsible AI, and current ML and AI practices.

What you'll need

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• 3+ years of relevant professional experience in Data Science, Machine Learning, Artificial Intelligence, analytics, or AI-focused software development.
• Hands-on experience preparing data, conducting exploratory analysis, building features, developing models, and evaluating results for practical business problems.
• Experience contributing to at least one Machine Learning, NLP, Generative AI, LLM, RAG, or Agentic AI solution through development, testing, integration, or production support.
• Working knowledge of model validation, experiment design, performance metrics, data quality, and reproducible development practices.
• Ability to complete a range of technical assignments and solve moderately complex problems within established policies, procedures, and architectural patterns.
• Effective written and verbal communication skills, including the ability to explain complex information clearly in straightforward situations.
• Strong collaboration, learning agility, attention to detail, and problem-solving skills.
• Proficiency in Python and SQL, with hands-on experience in Pandas, NumPy, Scikit-learn, Jupyter, data preparation, feature engineering, analysis, and visualization using Matplotlib, Seaborn, Tableau, or Power BI.
• Practical knowledge of supervised and unsupervised learning, model selection and evaluation, statistical analysis, experimentation, and foundational deep learning and NLP concepts using frameworks such as TensorFlow, PyTorch, Hugging Face, or SpaCy.
• Experience with prompt engineering, embeddings, vector search, RAG, LLM evaluation, document-based use cases, tool-enabled agents, and workflow orchestration using frameworks such as LangChain or LangGraph, with exposure to Azure OpenAI, Azure Machine Learning, MLflow, and model APIs.
• Experience with reusable Python code, REST APIs, Git, unit testing, CI/CD, containers, databases, data pipelines, and cloud services, with awareness of monitoring, observability, security, privacy, cost, explainability, and responsible AI practices.

Nice to have

• No preferred qualifications are specified in the source.

Details

• Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
• Interviews must be completed without AI tools or external prompts, and virtual backgrounds must be removed.