(Revised for SFIA-8 Alignment)
Overview
As a Data Scientist (Grade 3), you develop, prototype, and deliver analytical models that generate meaningful insights and decision support across the business. You combine statistical knowledge, machine learning techniques, and business acumen to discover opportunities in data and communicate outcomes effectively.
You will collaborate closely with Data Engineers, Analysts, and Subject Matter Experts to ensure that data science solutions are feasible, interpretable, and sustainable. You are responsible for framing complex problems, applying advanced analytics, and turning findings into actionable solutions — and ensuring those solutions are operationalised using modern cloud tooling and responsible MLOps practices.
This is an exciting opportunity for a curious and business-aware Data Scientist ready to apply advanced modelling techniques in a real-world, cloud-first environment.
See Digital and Data Solutions Career Map
Key Responsibilities
1. Problem Framing & Data Exploration
(SFIA: Data Analysis – DTAN Level 4/5)
- Collaborate with business teams to identify opportunities for data-driven insights.
- Translate ambiguous problems into clearly defined analytical objectives.
- Conduct exploratory data analysis to understand patterns, detect anomalies, and guide model development.
DS301: Problem Framing & Exploratory Analysis
2. Statistical Modelling & Machine Learning
(SFIA: Data Science – DSCI Level 4/5)
- Design, develop, and evaluate statistical and machine learning models.
- Apply supervised and unsupervised learning techniques such as regression, clustering, decision trees, and time series analysis.
- Track model performance using appropriate validation and scoring metrics.
DS302: Applied Modelling & Algorithm Development
3. Feature Engineering & Data Preparation
(SFIA: Data Engineering – DENG Level 4)
- Work with large datasets to design relevant features, impute missing values, and reduce dimensionality.
- Leverage feature stores and reusable transformations in collaboration with Data Engineers.
- Apply best practices for data wrangling, including reproducibility and version control.
DS303: Feature Engineering & Data Preparation
4. Visualisation & Storytelling
(SFIA: Specialist Advice – TECH Level 4/5)
- Create clear visual representations of data insights using tools like Power BI, matplotlib, or Plotly.
- Present findings in a compelling narrative that drives decision-making.
- Translate model outputs into accessible language for non-technical stakeholders.
DS304: Insight Communication & Storytelling
5. Model Operationalisation & MLOps
(SFIA: Systems Integration – INCA Level 4/5, Software Development – SWDN Level 4)
- Collaborate with engineering teams to deploy machine learning models to production.
- Package models as APIs using frameworks like Flask, FastAPI, or MLflow.
- Integrate models into data pipelines or Azure Machine Learning services.
- Apply version control, CI/CD pipelines, and automated retraining strategies.
- Monitor model performance in live environments and retrain based on drift or feedback.
DS305: Model Deployment, Monitoring & Lifecycle Management
6. Research, Experimentation & Continuous Learning
(SFIA: Innovation – INOV Level 4/5)
- Stay up-to-date with emerging techniques, algorithms, and tools.
- Prototype new solutions, A/B test approaches, and evaluate trade-offs.
- Contribute to an internal culture of experimentation and learning.
DS306: Applied Research & Innovation
7. Ethics, Fairness & Governance in Data Science
(SFIA: Information Security – SCAD Level 4)
- Ensure model interpretability, fairness, and compliance with data privacy regulations (e.g., GDPR).
- Contribute to ethical reviews and explainability assessments.
- Document modelling decisions, risks, and assumptions.
DS307: Data Ethics & Responsible AI
8. Agile Collaboration & Stakeholder Engagement
(SFIA: Stakeholder Engagement – RLMT Level 4)
- Collaborate with delivery teams to prioritise backlogs and define sprint goals.
- Participate in retrospectives and knowledge-sharing sessions.
- Support stakeholder workshops and model reviews.
DS308: Agile Working & Team Collaboration
What You Bring
✅ Core Technical Skills
- Strong proficiency in Python (pandas, scikit-learn, PyTorch, statsmodels, etc.) or R.
- Experience using SQL for querying and joining large datasets.
- Familiarity with MLflow, Azure ML, or Databricks for operationalising models.
- Confidence with packaging and deploying models via APIs or containers.
- Understanding of time series, classification, regression, clustering, and ensemble methods.
✅ Model Ops & Cloud Engineering
- Experience integrating models into pipelines and dashboards.
- Working knowledge of CI/CD, Docker, GitHub Actions, or Terraform for ML workloads.
- Knowledge of Azure cloud services for MLOps (Azure ML, Functions, Event Grid).
✅ Collaboration & Agile Working
- Ability to work in cross-functional teams with Engineers and Analysts.
- Comfortable iterating through ideas and engaging with technical and non-technical audiences.
- Familiarity with backlog management and Agile methodologies.
✅ Desirable Experience
- Use of Power BI to create prototyped visuals for model outputs.
- Exposure to ML interpretability frameworks like SHAP, LIME, Fairlearn.
- Experience building or contributing to internal Feature Stores or Model Registries.
- Familiarity with synthetic data generation or data augmentation techniques.
Why With Us?
🌍 Work on real-world challenges with tangible global impact.
🔄 Carry your models from concept to live deployment.
🧠 Learn and apply new approaches in a supportive, forward-thinking team.
💻 Use modern ML tooling on Azure cloud platforms.
🤝 Join a hybrid, people-first environment focused on quality, sustainability, and innovation.