People Data Scientist

Location: Newcastle, United Kingdom.

Role Summary: Join the People Analytics Centre of Excellence and help transform how we understand and leverage workforce data across the business. In this hybrid role (3 days in office, 2 days remote), you will build advanced analytics, machine‑learning models, and AI‑driven solutions that empower leaders to make evidence‑based decisions about talent, performance and the future of work.

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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 organizational network analysis, 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.
  

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• Strong proficiency in Python (Pandas, NumPy, Scikit‑learn, PyTorch/TensorFlow) and SQL.
  
• Experience working with HR data sources (Workday, SuccessFactors, Oracle HCM, LinkedIn Talent Insights) 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 & business data.
  
• Familiarity with MLOps principles, CI/CD, and deploying ML/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, 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.
  

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• 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.
  

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• 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 the 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.