Grundlegend

Data Scientist

london, south east england, United Kingdom Gesellschaft: JR UK Kunde / Arbeitgeber: Synergetic
Gepostet: 19.05.2026
Abschlussdatum: 03.07.2026
Berufsreferenz: 130cf2560650a39f17e0716a2d5c16ad

Stelleninformationen

Lage
london, south east england, United Kingdom
Gesellschaft
JR UK
Kunde / Arbeitgeber
Synergetic
Berufsreferenz
130cf2560650a39f17e0716a2d5c16ad
Auflistungstyp
Grundlegend
EU-Arbeitserlaubnis erforderlich
Nein
Gepostet
19.05.2026
Abschlussdatum
03.07.2026

Stellenbeschreibung

Full Stack Data Scientist - AI & Knowledge Systems


About the Role

We are seeking an exceptional Full Stack Data Scientist to join our clients innovation team. This role combines traditional data science expertise with software engineering capabilities to build end-to-end AI solutions. The ideal candidate will have a strong foundation in both developing sophisticated machine learning models and implementing them within production systems. You will work closely with cross-functional teams to transform concepts into scalable AI-powered products.


We are looking for candidates that can combine technical expertise with a true consulting approach.


Responsibilities

  • Design, develop, and implement advanced machine learning models and AI capabilities
  • Build and maintain knowledge graphs and causal inference systems
  • Create probabilistic models to address complex business problems
  • Scale AI solutions from proof-of-concept to MVP and full production
  • Collaborate with backend engineers on data pipelines and infrastructure
  • Work within solution architecture frameworks to ensure AI integration
  • Contribute to solution design and technical decision-making
  • Translate business requirements into technical specifications


Required Skills & Experience

  • Great experience combining data science with software engineering
  • Strong expertise in machine learning, with focus on causal ML and probabilistic modelling
  • Experience developing and implementing knowledge graphs
  • Proficiency in scaling AI solutions from concept to production
  • Working knowledge of backend systems, data pipelines, and ETL processes
  • Familiarity with cloud platforms, particularly Microsoft Azure
  • Understanding of microservices architecture and distributed systems
  • Experience with DevOps practices for AI/ML workflows (MLOps)
  • Strong programming skills in Python and related data science libraries
  • Demonstrated ability to work within solution architecture frameworks


Preferred Qualifications

  • Experience with multiple cloud providers beyond Azure
  • Familiarity with container orchestration (Kubernetes)
  • Knowledge of graph databases and query languages
  • Experience with deep learning frameworks
  • Background in NLP, computer vision, or reinforcement learning
  • Domain expertise across industries but familiar with Financial Services, Healthcare and Lifesciences, Industrials and Telecommunications and infrastructure would be a plus

Fähigkeiten

apply blended learning apply for research funding apply research ethics and scientific integrity principles in research activities build recommender systems Business Analytics Business Intelligence collect ICT data communicate with a non-scientific audience Computational Biology Computer Simulation conduct research across disciplines create data models Data Engineering data ethics Data Mining Data Models data quality assessment Data Science data visualisation software define data quality criteria deliver visual presentation of data demonstrate disciplinary expertise design database in the cloud design database scheme develop data processing applications develop professional network with researchers and scientists Digital Curation disseminate results to the scientific community draft scientific or academic papers and technical documentation empirical analysis establish data processes evaluate research activities execute analytical mathematical calculations Hadoop handle data samples Healthcare Analytics image recognition implement data quality processes increase the impact of science on policy and society information categorisation Information Extraction integrate gender dimension in research integrate ICT data interact professionally in research and professional environments interpret current data LDAP LINQ make data-driven decisions manage data manage data collection systems manage findable accessible interoperable and reusable data manage ICT data architecture manage ICT data classification manage intellectual property rights manage open publications manage personal professional development manage research data Marketing Analytics mathematical modelling MDX mentor individuals multidisciplinary research N1QL normalise data online analytical processing operate open source software perform data cleansing perform data mining perform project management perform scientific research promote open innovation in research promote the participation of citizens in scientific and research activities promote the transfer of knowledge publish academic research quantitative analysis query languages report analysis results Research Design resource description framework query language Scientific Computing scientific literature Social Network Analysis SPARQL speak different languages State Estimation statistical modeling techniques Statistics synthesise information teach in academic or vocational contexts think abstractly Unstructured Data use data processing techniques use databases use spreadsheets software visual presentation techniques write scientific publications XQuery

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