Basis

Senior Data Scientist (AI & Synthetic Intelligence)

catalunya, Spain Bedrijf: JR Spain Klant / Werkgever: Cint
Geplaatst: 28.05.2026
Sluitingsdatum: 12.07.2026
Functiereferentie: 879113521046172467232460

Functie-informatie

Locatie
catalunya, Spain
Bedrijf
JR Spain
Klant / Werkgever
Cint
Functiereferentie
879113521046172467232460
Vermeldingstype
Basis
EU-werkvergunning vereist
Nee
Geplaatst
28.05.2026
Sluitingsdatum
12.07.2026

Functiebeschrijving

Job Description

As a Senior Data Scientist at Cint, you will play a pivotal role in developing next-generation AI solutions that power our product portfolio. Collaborating closely with Product and Engineering teams, you will bridge the gap between traditional research data and synthetic intelligence. You will focus on the research, validation, and delivery of models—including Large Language Models (LLMs)—that augment high-quality human signals across the Cint Exchange. This role involves advanced data mining, robust data validation, and the development of sophisticated statistical and machine learning methodologies.

The ideal candidate can independently research, develop, and maintain high-impact solutions that align Cint’s AI capabilities with market research trends, directly influencing strategic decisions for Cint’s proprietary synthetic data platform.

Responsibilities

  • Lead the research, discovery, and development of machine learning models - specifically focused on synthetic row generation, open-ended text generation, and data augmentation.

  • Design and drive advanced statistical methods and complex experiments to validate LLM performance and synthetic modeling hypotheses.

  • Develop logic for on-demand and dynamic boosting capabilities, collaborating with Engineering to integrate these models into Cint Exchange fielding workflows.

  • Design and refine sophisticated profiling taxonomies, leveraging large-scale datasets to create syndicated audiences.

  • Independently manage complex project planning, development, and maintenance end-to-end with minimal supervision.

  • Partner with Product, Engineering and Strategy teams to align technical AI delivery with business goals, ensuring a seamless transition from MVP to scaled integration.

  • Create clear, effective prototypes and deliverables that explain and defend complex Generative AI concepts to both technical and non-technical audiences.


Qualifications

Qualifications Required:

  • Minimum 5 years of experience in a Data Science capacity, with a track record of leading complex projects.

  • A Master's degree (or equivalent) in Statistics, Data Science, or a related quantitative field.

  • Deep understanding of Generative AI and LLMs, particularly for applications in text generation and data synthesis.

  • Advanced knowledge of statistical techniques: hypothesis testing, sampling theory, experimental design, and causal inference.

  • Strong knowledge of a variety of ML techniques (e.g., clustering, regression, neural networks, etc.) and their real-world trade-offs.

  • Expert proficiency in Python (DS/ML stack) and experience with frameworks used for LLM development and fine-tuning.

  • Advanced SQL skills and experience working with large-scale databases.

  • Ability to research and adopt new methods while mentoring junior colleagues on their application.

Essential Qualities:

  • Highly accountable self-starter and quick learner, consistently motivated to deliver high-quality, impactful results.

  • Strong data-driven mindset with the ability to translate abstract business requests into actionable AI initiatives and solutions.

  • Excellent written and verbal communication skills, with the ability to explain and defend complex AI concepts to non-experts.

Nice to Have:

  • Direct experience with Synthetic Data Generation techniques and the evaluation of synthetic data quality/utility.

  • Experience with Prompt Engineering, RAG (Retrieval-Augmented Generation), or fine-tuning open-source LLMs for open-end generation.

  • Experience with probabilistic modeling, or advanced profiling techniques.

  • Familiarity with online market research or survey exchange platforms.

  • Experience using Databricks, Spark, or PySpark for large-scale workflows.


Additional Information

#LI-Remote

Our Values

Collaboration is our superpower

  • We uncover rich perspectives across the world
  • Success happens together
  • We deliver across borders.

Innovation is in our blood

  • We’re pioneers in our industry
  • Our curiosity is insatiable
  • We bring the best ideas to life.

We do what we say

  • We’re accountable for our work and actions
  • Excellence comes as standard
  • We’re open, honest and kind, always.

We are caring

  • We learn from each other’s experiences
  • Stop and listen; every opinion matters
  • We embrace diversity, equity and inclusion.

 

More About Cint

We’re proud to be recognised in Newsweek’s 2025 Global Top 100 Most Loved Workplaces®, reflecting our commitment to a culture of trust, respect, and employee growth.

In June 2021, Cint acquired Berlin-based GapFish – the world’s largest ISO certified online panel community in the DACH region – and in January 2022, completed the acquisition of US-based Lucid – a programmatic research technology platform that provides access to first-party survey data in over 110 countries.

Cint Group AB (publ), listed on Nasdaq Stockholm, this growth has made Cint a strong global platform with teams across its many global offices, including Stockholm, London, New York, New Orleans, Singapore, Tokyo and Sydney. (www.cint.com)

 

Additionally, in a world of AI, we want our candidates to understand our approach to the use of AI during the interview and hiring process, so we'd appreciate you reading our AI usage guide.

Vaardigheden

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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