Data Scientist

Key Responsibilities

• Develop data science and AI solutions: 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.

• Evaluate and improve solution quality: 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.

• Support deployment and engineering: Build reusable code, APIs, notebooks, pipelines, containers, model artifacts, monitoring components, and automated workflows with engineering and senior data science team members.

• Collaborate, communicate, and maintain standards: Translate requirements into assigned deliverables, communicate insights through visualizations, prototypes, reports, and documentation, and manage code, data, and experiments in line with security, privacy, governance, version control, responsible AI, and current ML and AI practices.

Required Qualifications

• 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.

Technical Skills

• Programming and analytics: 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.

• Machine Learning, statistics, and NLP: 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.

• Generative AI and Agentic AI: 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.

• Engineering, cloud, and governance: 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.

Success Measures

Success in this role is measured through:

• Accurate, timely, and well-documented data preparation, analysis, and reporting.

• Reliable contributions to Machine Learning, Generative AI, and Agentic AI development, testing, integration, and production support.

• Measurable improvement in model or application quality, including accuracy, relevance, reliability, latency, cost, or user experience.

• Clear visualizations and actionable insights that support product and business decisions.

• Reusable, maintainable code and effective automation of repeatable data and AI workflows.

• Consistent adherence to data quality, security, governance, responsible AI, documentation, and team development standards.

• Effective collaboration with senior data scientists, engineering teams, product partners, and business stakeholders.

• Demonstrated growth in technical capability, business understanding, and ownership of moderately complex assignments.

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.