Data Scientist

Applaudo

. Build and evaluate ML approaches for company/entity matching .

Posted 10/8/2026full-timeRemote • PeruMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning and Data Science, with a strong focus on developing and evaluating LLM-based matching approaches, embeddings, and experimental design. Proficient in Python and SQL, with a solid understanding of model evaluation, scalability, and inference economics.

Highest-signal resume keywords

Machine Learning FundamentalsPython ProgrammingSQL SkillsLLM Application ExperienceExperimental Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningEmbeddingsSemantic SimilarityClassificationNatural Language ProcessingNeural NetworksTransformer ArchitecturesModel EvaluationError AnalysisScalability

Soft Skills

Analytical MindsetCommunication SkillsProblem-SolvingAutonomyIntellectual Honesty

Tools & Technologies

TensorFlowPyTorchPyCaretSparkSnowflakeDatabricksBigQuery

Industry Keywords

Entity ResolutionRecord LinkageDeduplicationRankingSimilarity ScoringMultilingual DatasetsFirmographic Data

Tech Stack

Tools & technologies

BigQueryPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Build and evaluate ML approaches for company/entity matching
Develop embedding and LLM-based matching approaches
Develop scoring and ranking methodologies to identify true matches and distinguish them from duplicates, lookalikes, and unrelated entities
Work with messy data, including names, aliases, domains, websites, firmographic attributes, multilingual records, and data hierarchies
Define benchmark datasets, metrics, baselines, and error-analysis processes
Design and execute experiments to validate hypotheses
Compare LLM-assisted approaches against lower-cost alternatives
Analyze model behavior, edge cases, and trade-offs
Consider inference economics and scalability from the beginning
Communicate experimental findings and recommendations to engineering and business stakeholders
Independently establish experimental pipelines and research approaches
Clearly document both successful and unsuccessful experiments

Requirements

What you’ll need

5+ years of professional Data Science / Machine Learning experience
Strong applied Machine Learning fundamentals
Excellent Python and SQL skills
Hands-on experience with embeddings and semantic similarity
Practical experience applying LLMs to real-world problems
Experience with supervised and unsupervised learning
Strong experience with classification and NLP
Working knowledge of neural networks and transformer architectures
Hands-on experience with TensorFlow, PyTorch, PyCaret, or equivalent ML frameworks
Experience retraining or maintaining classification models in production
Strong experimental design and model evaluation skills
Experience defining baselines, metrics, test sets, and error-analysis processes
Ability to evaluate model quality and demonstrate measurable improvements
Strong understanding of scalability and ML inference costs
Strong English communication skills
Entity resolution, record linkage, or deduplication experience (nice-to-have)
Ranking and similarity scoring (nice-to-have)
Retrieval, clustering, or candidate-generation techniques (nice-to-have)
LLM/embedding solutions designed for cost and scale constraints (nice-to-have)
Spark, Snowflake, Databricks, or BigQuery (nice-to-have)
Experience with company, domain, website, or firmographic data (nice-to-have)
Experience working with multilingual datasets (nice-to-have)
Strong analytical and experimental mindset
Intellectual honesty and willingness to communicate negative results
Strong autonomy and self-direction
Excellent written and verbal communication
Ability to defend technical recommendations with stakeholders
Strong problem-solving skills
Comfort working with ambiguity and large-scale datasets
Ability to balance model quality, cost, and scalability

Benefits

Comp & perks

Remote work option
Opportunity to learn fast and take ownership
Collaboration with strong teams
Investment in modern ways of working