We’re looking for a Data Scientist to help us apply statistics, machine learning, and data analysis to real customer and product problems at scale. In this role, you’ll work hands on with structured, transactional data, building models and analyses that directly inform product behaviour and business decisions.
You’ll combine strong foundations in statistics and machine learning with practical experience working in AWS based data environments. You’ll collaborate closely with software engineers and product teams to ensure your work is robust, reproducible, and usable in production systems.
You’ll also contribute to AI enabled and LLM supported use cases (e.g. evaluation, analysis, retrieval based insights), where they add value — without being expected to design or operate AI platforms yourself.
This is a hybrid role, requiring three days per week in our Newcastle office.
What You’ll Do
- Analyse large, complex datasets to identify patterns, trends, and anomalies
- Apply statistical techniques and machine learning models to well defined business problems
- Build, evaluate, and iterate on models such as classification, regression, clustering, anomaly detection, or forecasting
- Use AWS based data and analytics tools to access data, run experiments, and support model workflows
- Design and interpret offline evaluations and experiments, clearly explaining results and trade offs
- Partner with engineers to ensure models and insights can be safely integrated into production systems
- Support AI and LLM enabled features through data preparation, evaluation, and analytical input
- Communicate findings clearly to both technical and non technical stakeholders
What You’ll Be Working On
- Financial and transactional datasets (e.g. accounting, payments, operational data)
- Customer behaviour and product usage analysis
- Risk, quality, or anomaly focused modelling problems
- Feature engineering and dataset development
- Supporting AI driven features with sound data science and evaluation
First 90 Days:
- 30 days - Learn our data landscape, AWS environment, and key business domains. Contribute to exploratory analysis and support existing modelling work.
- 60 days - Deliver a well scoped data science contribution end to end — from hypothesis and data preparation through to model evaluation and stakeholder read out.
- 90 days - Independently own a defined problem area, producing reliable models or analyses that influence decisions or product behaviour. Contribute improvements to shared data science practices.
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How Success Will Be Measured:
- Soundness of statistical reasoning and modelling choices
- Quality, clarity, and reproducibility of analysis and code
- Model performance measured against agreed evaluation metrics
- Practical impact on product decisions or customer outcomes
- Effective collaboration with engineering and product partners
Must Have Skills:
- Experience applying statistics and machine learning in an applied or commercial setting
- Strong Python skills for data analysis and modelling
- Strong SQL skills and comfort working with large structured datasets
- Hands on experience working with AWS for data analysis or machine learning workflows e.g. accessing data, running jobs, supporting model pipelines, or collaborating on deployment
- Understanding of model evaluation, bias, and common data science pitfalls
- Clear written and verbal communication skills
- Ability to work effectively in cross functional teams
Nice to Have Skills:
- Experience with financial, accounting, or transactional data
- Familiarity with AWS analytics or ML services (e.g. data storage, compute, managed ML tools)
- Experience with experimentation or causal analysis techniques
- Exposure to LLMs or AI enabled systems, particularly evaluation or analysis
- Experience collaborating with engineers on productionisation and monitoring
hackajob is partnering with Sage to fill this position. Create a profile to be automatically considered for this role—and others that match your experience.
