Básico
Data Scientist
Publicado: 20.05.2026
Fecha de cierre: 04.07.2026
Referencia laboral: b80ca28c5f7faee05667bf6ac6cd646a
Información del puesto
Ubicación
Greater London, England, United Kingdom
Compañía
Bitrecruit
Cliente / Empleador
Occupop
Referencia laboral
b80ca28c5f7faee05667bf6ac6cd646a
Tipo de listado
Básico
Se requiere permiso de trabajo de la UE
No
Publicado
20.05.2026
Fecha de cierre
04.07.2026
Descripción del puesto
As a Data Scientist within the Analytics Team, you will contribute to data-driven strategies for our clients. Working closely with the Data & Analytics Manager and senior colleagues, you will deliver data science projects and collaborate with stakeholders across data strategy, sales, account management, delivery and marketing. You will bring solid technical skills and commercial awareness to deliver data solutions that drive measurable operational performance. This is a hands-on role - ideal for someone who thrives on translating data into actionable insight and producing high-quality outcomes.ResponsibilitiesDeliver data science projects, from problem definition through to actionable insights and presentation of resultsDevelop and apply predictive modelling, supervised and unsupervised machine learning techniques to optimise client operations and business outcomesBuild and maintain data pipelines, ensuring data quality, consistency, and integrity across multiple sources and formatsTranslate complex analyses into clear, commercially relevant recommendations for clients and internal stakeholdersWork with client teams to identify analytical opportunities, support marketing strategy, and quantify the impact of data-driven decision-makingSupport pre-sales and client engagement, helping to demonstrate the value of data insightFollow best practices in data science, reproducible research, and ethical AICollaborate cross-functionally to enhance the company's products and marketing data solutionsWhat Success Looks Like in the RoleDelivery of impactful, high-quality analytics that directly inform and improve client marketing outcomesBuilding trust and credibility with clients as an analytical consultantRegular iteration on our machine learning methodologies, tools, and frameworksConsistent demonstration of technical excellence and commercial insight in all project deliverablesMeasurable contribution to the enhancement of Sagacity's data science and analytics product suiteCompetencies and Experience2+ years' experience in data science, analytics, or statistical modelling, ideally with commercial experience within the Telecoms, Banking or Utilities industries; or within a data-related consultancyEducated to degree level (postgraduate preferred) in a quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similarWorking knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLPProficient in Python and/or R, with strong experience in data quality, model development and feature engineeringStrong command of SQL and familiarity with data engineering environments such as Databricks or similarSkilled in data visualisation and storytelling using tools such as Power BI, Tableau, Plotly, or SigmaDemonstrated ability to translate technical findings into strategic recommendations for non-technical audiencesCommercially aware, with proven success in applying analytics to solve business problemsStrong communicator; able to engage stakeholders and present findings with clarity and confidenceSelf-motivated, organised, and proactive, with the ability to manage multiple priorities and stakeholders in a fast-paced environmentWilling to travel across the UK for client engagementsMust have the right to work in the UK and a commitment to ongoing professional development
Habilidades
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