Dasar

Data Scientist

catalunya, Spain Perusahaan: JR Spain Klien / Pemberi Kerja: Lumora Solutions
Diposting: 23.05.2026
Tanggal penutupan: 07.07.2026
Referensi pekerjaan: 19cba4c0ea427fec6b5c7ae59c4133fd

Informasi pekerjaan

Lokasi
catalunya, Spain
Perusahaan
JR Spain
Klien / Pemberi Kerja
Lumora Solutions
Referensi pekerjaan
19cba4c0ea427fec6b5c7ae59c4133fd
Jenis daftar
Dasar
Izin kerja UE diperlukan
Tidak
Diposting
23.05.2026
Tanggal penutupan
07.07.2026

Deskripsi pekerjaan

Data Scientist Salary: €50,000 - €70,000 Location: Barcelona 3x Per Week As a Data Scientist, you will take full ownership of projects from initial problem identification through to delivering actionable insights and scalable models. You’ll blend analytical expertise, commercial awareness, and technical capability to transform data into meaningful business outcomes. What you will be doing: Lead full lifecycle data projects, from scoping to deployment Analyse user behaviour to drive performance, engagement, and revenue insights Run A/B tests, define KPIs, and deliver clear, data-driven conclusions Build predictive models and segmentation to support business decisions Ensure data quality and apply AI tools to enhance workflows and impact What you will bring: Experience working with large datasets and data structures Solid grasp of statistics, experimentation, and A/B testing Hands-on experience with machine learning in production use cases Familiar with cloud platforms (ideally GCP) and modern data stacks Degree in Engineering, Computer Science, Statistics, or a similar field Experience in the following industries: SaaS / Gaming / Digital Media (Nice to Have) Why Join: Collaborative organisation in fast moving enviroments

Keterampilan

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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