Data Scientist
- Compensation
- £50K–£85K / yr
- Location
- Cheltenham
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Experience of Python-based data science and ML, and some exposure to NLP or graph analytics.
- Comfortable with sensitive data.
- Able to explain complex analysis to non-specialists.
Full description
Are you a Data Scientist who is looking to get involved in projects and data relating to our national security?Data Scientist (AI/ML)Location: London, Surrey, Cheltenham or Manchester
Clearance: Active eDV Clearance Required
Salary: £50,000-£85,000 per annum + DV Bonus
If you're tired of building models that never leave the notebook, this one goes live.
You'd join a specialist security and intelligence consultancy that works on live government programmes, putting ML into operational systems that analysts and mission teams use every day. You'll sit in a small, multi-disciplinary agile team with data engineers, software engineers and the end users themselves, so you see what your work changes.
You won't be "the data person" bolted onto someone else's delivery team. You'll help shape the AI/ML approach, get close to the customer, and have a say in how models are built, validated and deployed in secure environments.
What you'll be working on
- ML and NLP models for intelligence data: pattern detection, data matching, anomaly spotting
- Automated triage and analysis of large, messy, multi-source datasets (sensor, geospatial, comms)
- Graph analytics and time-series work
- Data and ML pipelines built for production, not demos
- Dashboards and tools that non-technical users can act on
- Python (Pandas, scikit-learn, PyTorch/TensorFlow), Spark and AWS
Why it's worth a look
- Real operational deployment, not endless proofs of concept
- Direct contact with the people using what you build
- Room to lead on AI/ML strategy and best practice as you grow
Must have:
- Experience of Python-based data science and ML, and some exposure to NLP or graph analytics.
- Comfortable with sensitive data.
- Able to explain complex analysis to non-specialists.
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