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.

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.

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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📌 Data Scientist (Madrid)
🏢 Bain
📍 Madrid