#EG Data Scientist at NCS3

As a Data Scientist, you will design, develop, and deploy advanced analytics and machine learning solutions that uncover hidden insights from large, complex datasets. You will work closely with business stakeholders, project managers, and engineering teams to translate real-world business challenges into production-ready data science solutions. This role combines hands-on analytics development, applied research, and client advisory responsibilities, supporting organisations on their data science and AI journey.   What will you do? Applied Data Science & Advanced Analytics • Translate customer pain points into clear analytical problem statements and solution architectures. • Design, build, and iterate end-to-end data science workflows, from data ingestion and preprocessing to feature engineering, modelling, and deployment. • Apply statistical analysis, machine learning, NLP, optimisation, and simulation techniques to solve complex business problems. • Perform statistically sound model validation and clearly justify model selection and performance. Model Engineering & Production Deployment • Build scalable, efficient machine learning models for deployment in production systems. • Operationalise analytics workflows using Python/R and distributed processing frameworks such as Apache Spark. • Deploy and manage models using containerisation and orchestration tools (e.g. Docker, Kubernetes). • Leverage LLMs to build GenAI or Agentic AI solutions where appropriate. Insights Communication & Visualisation • Design and develop impactful dashboards and visualisations to communicate actionable insights. • Present results, learnings, and recommendations clearly to both technical and non-technical audiences. • Act as a trusted adviser to clients in conceptualising and evaluating advanced analytics solutions. Collaboration & Delivery • Work closely with project managers and technical leads to provide regular status updates and refine analytics requirements. • Contribute to data architecture and engineering decisions that support analytics use cases. • Participate in interdisciplinary teams delivering projects using Agile or Waterfall methodologies. Knowledge Sharing & Mentorship • Contribute to internal communities of practice and special interest groups. • Mentor and upskill junior data scientists and peers, depending on seniority.