#EG Junior Data Scientist

NCS is looking for a data scientist who can leverage advanced techniques in machine learning and AI to extract insights from complex data sets and address challenging business problems.

The data scientist will work closely with business users, project managers, and data engineers to develop modern machine learning solutions capable of automating complex business processes. The role requires the practitioner to also be a trusted adviser to our clients in conceptualising advanced analytics solutions, and helping to enable their journey in data science and AI.

Technical Delivery

• Work with senior data scientists and stakeholders to translate customer pain-points into problem statements, propose suitable ML approaches, and engagingly present findings to both technical and non-technical audiences.
• Develop and manage entire end-to-end ML lifecycle: scoping of data inputs, data cleaning and pre-processing, feature engineering, training and optimising models, and deploying models to production.
• Present statistically sound model validations to justify model selection and performance.
• Write clean, maintainable Python code and construct end-to-end pipelines for model training and inference; participate in code reviews.
• Design and develop sophisticated visualisations, reports and dashboards to profile and explain actionable insights to business users.
• Contribute to data architecture engineering decisions to support ML pipeline building.
• Work closely with project manager and technical leads to provide regular status reporting, and support them to refine issues/problem statements and propose/evaluate relevant data science solutions.

Client & Stakeholder Engagement

• Support client workshops and daily stakeholder interactions: requirements discovery, solution presentations, sprint demos and retrospectives.
• Translate ambiguous client requirements into well-defined technical user stories and acceptance criteria.
• Communicate technical trade-offs and design decisions in clear, non-jargon language appropriate for business stakeholders.
• Manage client expectations proactively; surface risks early and propose mitigations.

Experience & Education

• 2–4 years of hands-on ML and data science solution development experience.
• Degree in Computer Science, Data Science, Statistics, Mathematics, Information Systems, or equivalent quantitative discipline.
• Proven delivery of complex end-to-end AI/ML projects.
• Strong Python programming: Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow; comfortable writing modular, testable code.
• Solid understanding of ML fundamentals: feature engineering, supervised and unsupervised modelling techniques, model evaluation, bias-variance trade-off, hyperparameter optimisation.