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. The CoroSM 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. 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. The CoroSM 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. 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.



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 Evaluate AI and machine learning techniques while balancing accuracy, scalability, and cost Improve Experimentation & Model Quality Develop robust approaches to measuring match quality, including precision, recall, false positives, false negatives, confidence, coverage, and manual review requirements Conduct detailed error analysis to understand model behavior and identify opportunities for improvement Partner closely with data and software engineers to turn promising prototypes into production‐ready matching solutions Help determine the right matching techniques for different data tiers, confidence levels, and cost profiles Clearly communicate trade‐offs across model quality, scale, cost, latency, explainability, and operational risk 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 ~ 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 ~ Strong experimental judgment, including the ability to define baselines, evaluation metrics, test sets, and error analyses ~ 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 #

📌 Marketing Data Scientist (Madrid)
🏢 Bain u0026
📍 Madrid