Tech – Data Scientist: EmPOWER Workplace Fairness

Transforming employment mediation through predictive analytics and GenAI Mediation Pattern Analysis: Master advanced data mining and statistical analysis techniques to evaluate TADM mediator resolution strategies, identifying successful mediation patterns and quantifying effectiveness of different dispute resolution approaches across various employment scenarios. AI Training Dataset Development: Learn to transform qualitative mediation expertise into structured training data, creating comprehensive playbooks and knowledge bases that capture mediator decision-making processes for GenAI model development and fine-tuning. Predictive Dispute Modelling: Develop machine learning models to predict dispute outcomes and recommend optimal resolution pathways, using historical case data to build algorithms that can guide parties toward successful self-service settlements. GenAI Model Evaluation and Optimisation: Gain expertise in evaluating conversational AI performance for legal advisory contexts, implementing robust testing frameworks to ensure AI-generated advice aligns with established mediation principles and regulatory compliance. Automated Negotiation System Design: Build intelligent e-negotiation frameworks that provide contextual guidance to both claimants and respondents, creating self-service dispute resolution systems that reduce dependency on human mediators while maintaining fairness and legal accuracy.

Statistical Analysis and Data Mining: Proficiency in statistical methods, hypothesis testing, and exploratory data analysis using Python (pandas, scikit-learn) or R. Experience with pattern recognition, clustering, and classification techniques through coursework or academic projects. Machine Learning Fundamentals: Understanding of supervised and unsupervised learning algorithms, model evaluation metrics, and cross-validation techniques. Familiarity with predictive modelling concepts and experience building basic ML models for classification or regression problems. Natural Language Processing for Legal Text: Knowledge of text analytics, sentiment analysis, and document classification methods. Experience processing unstructured text data, understanding legal/regulatory language patterns, and extracting insights from textual documents. Data Visualisation and Reporting: Skills in creating compelling data visualisations using tools like matplotlib, seaborn, or Tableau to communicate analytical findings. Ability to translate complex statistical results into clear insights for non-technical stakeholders and policy makers.