Senior Data Scientist
Listed on 2026-01-14
-
IT/Tech
AI Engineer, Data Scientist, Data Analyst, Machine Learning/ ML Engineer
Department/Division
:
Innovation
Duration
:
Permanent
Location
: UK
Reports to
:
Data Science and AI Governance Lead
Direct Reports
:
None
Type of Role
:
Hybrid
Budget Responsibilities
:
No
Reference Number
: 9105
Reporting to our Data Science and AI Governance Lead, you will be part of a growing data solutions function that is passionate about innovation in the legal sector. You will develop data-driven solutions that optimise legal processes, enhance decision-making, and deliver predictive insights valued by our clients globally. You will leverage your deep technical knowledge to build AI applications, intelligent agents, and API-based solutions, creating proof of concepts and transitioning these prototypes into scalable, cloud-based applications.
In this role, your curiosity, creativity and problem‑solving will be key. You will collaborate with cross‑functional teams across legal, innovation, IT, and external partners, communicating effectively with stakeholders and presenting focused insights. As a key member of our dynamic team, you will also help nurture a culture of continuous learning, curiosity and innovation by upskilling in AI and data literacy, ensuring that we remain at the forefront of legal tech and AI advancements while growing together as a function.
Key Responsibilities- Innovation:
Conceptualise and develop innovative legal tech solutions utilising machine learning, artificial intelligence, and data analytics. Design and execute proof‑of‑concepts, moving successful prototypes into cloud‑native, production‑ready applications using modern AI frameworks such as the OpenAI Developer Kit. - Data strategy:
Collaborate with data governance and information security teams to establish robust data strategies, ensuring data integrity, compliance, and security in all legal tech initiatives. Apply your cloud computing expertise to build and manage scalable data pipelines and services. - Collaboration:
Partner with legal teams, data solutions teams, IT, and external experts to translate business needs into practical, high‑impact AI solutions. Communicate insights and progress through clear, compelling technical presentations and client demos, ensuring alignment with business strategies. - Research:
Stay curious and abreast of emerging technologies, trends, and methodologies in legal tech and data science. Identify opportunities to enhance processes and drive innovation through data science, quantitative analysis, and applied machine learning. Actively experiment with emerging AI tools and models, translating curiosity into tangible improvements across workflows and client‑facing solutions. - Development:
Liaise with internal and external development resources, overseeing project timelines, deliverables and quality of work, ensuring alignment of projects to the UKIME Innovation strategy. Utilise your proficiency in Python (and relevant libraries such as OpenAI, Lang Chain, Llama Index, Pandas, Num Py) to design, develop, and deploy end‑to‑end AI systems, API integrations, and ETL/ELT pipelines in the cloud. - Training and support:
Enhance the AI and data literacy across the team by developing training materials and leading workshops or informal knowledge‑sharing sessions.
- A deeply curious, experimental, and proactive mindset, with a passion for exploring emerging AI capabilities and continuously learning to push the boundaries of innovation.
- Extensive experience in data science and analytics, backed by a strong quantitative background (e.g., Statistics, Mathematics, Engineering, Bioinformatics, Computer Science, or related fields).
- Proficiency in Python and SQL, with deep expertise in Python libraries for data analysis (such as Pandas and Num Py) and hands‑on experience integrating LLM APIs (e.g., OpenAI, Anthropic, Hugging Face).
- Strong understanding of LLM concepts, API orchestration, and agentic workflows, with a proven track record in designing, developing, and deploying AI solutions in Python.
- Hands‑on experience with data engineering tasks, including building ETL/ELT pipelines, containerisation (Docker), and API development and integration.
- Familiarity with MLOps and LLMOps…
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