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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.
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.
- 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
- 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
- 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
- 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
- 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
