People Data Scientist

This is a hybrid role working 3 days a week in the office and 2 from home. Key Responsibilities

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• Develop predictive and prescriptive people analytics models (attrition, skills, workforce planning, D&I insights, forecasting).
  
• Translate workforce challenges into experiments, insights, and actionable recommendations.
  
• Build AI-powered HR solutions, including NLP, generative AI, and LLM applications.
  
• Conduct ONA, workforce segmentation, and employee sentiment analysis.
  
• Partner with HRIS, engineering, and business teams to design scalable data pipelines and deploy ML/AI models.
  
• Create dashboards and visualisations that bring workforce insights to life for leaders.
  
• Support evidence-based decision-making across HR and the wider business.
  Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow, and experience with AI frameworks for deep learning and generative models) and SQL.
  
• Experience working with HR data sources (Workday, SuccessFactors, Oracle HCM, LinkedIn Talent Insights, etc.) or related workforce datasets.
  
• Knowledge of people analytics methodologies such as attrition modelling, pay equity analysis, employee lifetime value, skills inference, or organisational network analysis.
  
• Familiarity with big data frameworks (Spark, Databricks, Dask) and cloud platforms (AWS, Azure, GCP).
  
• Knowledge of Snowflake and experience integrating with HR and business data.
  
• Familiarity with MLOps principles, CI/CD, and deploying ML and AI models in production environments, including monitoring and retraining pipelines.
  
• Strong understanding of machine learning algorithms for classification, regression, clustering, and time series forecasting, plus exposure to advanced AI techniques such as NLP, large language models (LLMs), and generative AI.
  
• Experience with data visualisation tools (Tableau, Power BI, or Python-based libraries).
  
• Excellent problem-solving skills and ability to translate complex technical analyses into clear, actionable insights for non-technical audiences.
  
• Familiarity with vector databases, embedding-based retrieval, and prompt engineering to support AI-enabled HR solutions.
  
• Understanding of ethical AI principles, bias detection, and responsible AI practices in HR contexts. Degree in a quantitative discipline (applied mathematics, statistics, computer science, economics, organisational psychology, or related field).
  
• Demonstrable experience in exploratory data analysis, feature engineering, and predictive modelling.
  
• Experience with Python, Scikit-learn, and PyTorch. Ideally with exposure to PySpark, Snowflake, AWS, and GitHub (MLOps practices).
  
• Knowledge of AI model evaluation techniques, including prompt optimisation and performance benchmarking.
  Benefits video - https://youtu.be/TCMtTYUUiuU
  
• Generous bonuses and pension scheme: Up to 8% matched pension contribution plus 2% top-up by Sage.
  
• 25 days of paid annual leave with the option to buy up to another 5 days
  
• Paid 5 days yearly to volunteer through our Sage Foundation
  
• Enhanced parental leave
  
• Comprehensive health, dental, and vision coverage
  
• Work away scheme for up to 10 weeks a year
  
• Access to various helpful memberships for finances, health and wellbeing