20 publicaciones de empleo destacadas de data scientist / ai en madrid, md (¡Están contratando!)

Data Scientist
Bain & Company Inc —Madrid, Madrid provincia
- Jornada completa
- Máster
- SQL
- Machine learning
19h
GCP AI Engineer
BIP - Business Integration Partners —Madrid, Madrid provincia
- Jornada completa
- Español
- Azure
- Informática
Data Engineering Manager
Wunderman Thompson MAP —Madrid, Madrid provincia
- CRM software
- Azure
- Salesforce
GCP Data Engineer
BIP - Business Integration Partners —Madrid, Madrid provincia
- Jornada completa
- Java
- SQL
- Machine learning
Senior Technical Project Manager - Martech
VML/WPP Enterprise Solutions —Madrid, Madrid provincia
- CRM software
- Oficina
- AWS
Vice-President - AA&AT - Cross Analytics Global Markets
BBVA —Madrid, Madrid provincia
- Jornada completa
- CRM software
- Español
- Doctorado
Senior Technical Project Manager - Martech
Wunderman Thompson MAP —Madrid, Madrid provincia
- CRM software
- Oficina
- AWS
Senior Data Engineer
Wunderman Thompson MAP —Madrid, Madrid provincia
- Azure
- Oficina
- SQL
Senior Manager Data Science
Solera —Madrid, Madrid provincia
- Jornada completa
- Inglés
- Machine learning
- Python
Value Consultant - Data Analytics & Supply Chain Transformation
Celonis —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Español
- Oracle
- Informática
- Vacaciones pagadas
Senior Python Developer
EPAM Systems, Inc. —Madrid, Madrid provincia
- Azure
- Informática
- DevOps
- Vacaciones pagadas
- Programa de recomendación
Senior Manager - Data Science (Martech/CRM)
Wunderman Thompson MAP —Madrid, Madrid provincia
- CRM software
- Azure
- Salesforce
Senior Data Insights Consultant
MAP —Madrid, Madrid provincia
- Inglés
- Oficina
- SQL
Solicitud rápida
8d
GCP Senior AI Engineer
BIP - Business Integration Partners —Madrid, Madrid provincia
- Jornada completa
- Español
- Informática
- Inglés
Senior Generative AI Tech Lead / Architect (m/f/d)
Siemens —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Azure
- Doctorado
- Informática
11d
Enterprise Solutions Consultant II
Halliburton —Madrid, Madrid provincia
- Media jornada
- Ventas
- Marketing
13d
Senior AI Engineer / AI Architect
BIP - Business Integration Partners —Madrid, Madrid provincia
- Jornada completa
- Azure
- Informática
- Ventas
Global Creative Operations Lead
Syngenta Crop Protection —Madrid, Madrid provincia
- Jornada completa
- Inglés
- Marketing
- Seguro de vida
- Plan de pensiones
Solicitud rápida
Workload Orchestration Engineer
Roche —Madrid, Madrid provincia
- Jornada completa
- Informática
- Grado
- Linux
Management & Technology Consultant - Supply Chain
Celonis —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Español
- Oracle
- Informática
- Vacaciones pagadas

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Data Scientist

Bain & Company Inc -
Madrid, Madrid provincia

Solicitar ahora

Detalles del empleo

Contrato indefinido | Jornada completa
Hace 19 horas

Cualificaciones

TensorFlow
Derecho
Spark
Máster
SQL
Machine learning
NLP
Escalabilidad
Cloud
SaaS
Python

Descripción completa del empleo

WHAT MAKES US A GREAT PLACE TO WORK

We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.

WHO YOU’LL WORK WITH

About Coro℠

The Coro℠ business unit brings together Bain’s proprietary suite of software-as-a-service (SaaS) and data-as-a-service (DaaS) tools, including cloud-based software, online capability assessments, and advanced analytics, focused on enabling Commercial Excellence for B2B companies.

You’ll work closely with data scientists, data engineers, and software engineers to develop sophisticated approaches to entity resolution at scale. Your focus will be on experimentation and model quality, while your engineering partners will help bring successful approaches into production.

WHERE YOU’LL FIT WITHIN THE TEAM

As a Senior Applied Data Scientist, you’ll focus on one of the most challenging problems in large-scale data: determining when records from different sources refer to the same real-world business.

You’ll develop and test machine learning, embedding, and large language model (LLM) approaches that improve how we match and resolve complex entity data. You’ll explore how far emerging foundation-model techniques can improve match quality while ensuring solutions remain practical, scalable, and cost-effective across hundreds of millions of entities.

This is a highly applied role where you’ll have the opportunity to experiment with emerging AI techniques, measure their impact, and work with engineering teams to turn the strongest ideas into scalable solutions.

WHAT YOU’LL DO

Develop Smarter Entity Matching

Build and evaluate machine learning, embedding, and LLM-based approaches for entity resolution
Improve how our systems handle complex and messy data, including name variations, aliases, domains, websites, firmographic attributes, multilingual records, and data hierarchies
Develop scoring and ranking approaches that distinguish genuine matches from lookalikes, duplicates, and unrelated entities
Evaluate AI and machine learning techniques while balancing accuracy, scalability, and cost
Design solutions for large-scale use, identifying where sophisticated models add value and where more efficient approaches can achieve comparable results

Improve Experimentation & Model Quality

Develop robust approaches to measuring match quality, including precision, recall, false positives, false negatives, confidence, coverage, and manual review requirements
Help build trusted benchmark datasets to compare new approaches with existing matching methods before production rollout
Explore LLM-assisted review and validation both as a matching technique and as a benchmark for more scalable approaches
Translate ambiguous matching challenges into clear hypotheses, experiments, metrics, and recommendations
Conduct detailed error analysis to understand model behavior and identify opportunities for improvement

Bring Successful Approaches to Production

Partner closely with data and software engineers to turn promising prototypes into production-ready matching solutions
Provide clear model specifications, expected behaviors, evaluation results, edge cases, and rollout criteria
Help determine the right matching techniques for different data tiers, confidence levels, and cost profiles
Measure impact, diagnose regressions, and recommend improvements to models and matching logic
Clearly communicate trade-offs across model quality, scale, cost, latency, explainability, and operational risk

ABOUT YOU

Required Qualifications

5–8 years of relevant professional experience in applied data science, machine learning, or a related field
Strong applied machine learning expertise, including hands-on experience building and evaluating models using real-world data
Excellent Python and SQL skills
Practical experience with embeddings, semantic similarity, LLMs, or related AI techniques
Hands-on experience training supervised and unsupervised models, including classification and NLP applications
Working knowledge of neural networks and transformer architectures
Experience with machine learning frameworks such as TensorFlow, PyTorch, or PyCaret
Experience retraining taxonomy classifiers or maintaining classification models in production
Strong experimental judgment, including the ability to define baselines, evaluation metrics, test sets, and error analyses
Ability to clearly explain model behavior, technical trade-offs, and edge cases to engineering and business stakeholders

Preferred Qualifications

Experience with entity resolution, record linkage, deduplication, or similar matching problems
Experience with ranking, similarity scoring, retrieval, clustering, or candidate-generation techniques
Experience applying LLMs or embeddings to large-scale business problems where performance, scalability, and cost are important considerations
Exposure to large-scale data platforms such as Spark, Snowflake, Databricks, or BigQuery
Familiarity with company, domain, website, firmographic, or other business-entity data

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