About the role

Build and evaluate machine learning approaches for entity matching, including the development of embedding and LLM-based methodologies. Define benchmark datasets, metrics, and error-analysis processes while communicating findings to stakeholders.

What they look for

Machine Learning Python SQL Embeddings Semantic Similarity LLMs NLP Classification Supervised Learning Unsupervised Learning Neural Networks Transformer Architectures TensorFlow PyTorch Experimental Design Model Evaluation

Requirements

Requires 5+ years of professional experience in Data Science or Machine Learning with strong Python and SQL skills. Candidates must have hands-on experience with LLMs, NLP, and model evaluation in production environments.

Full description

Job Description

About You

You are an experienced Data Scientist with strong applied Machine Learning expertise and a track record of building and evaluating models using real-world, messy, large-scale data. You are comfortable working with embeddings, semantic similarity, LLMs, NLP, classification, and both supervised and unsupervised learning. You approach ambiguous problems through structured experimentation, clearly defined hypotheses, baselines, metrics, and error analysis.

You are highly autonomous, intellectually honest about experimental results, and able to clearly communicate technical recommendations and trade-offs to engineering and business stakeholders.

You Bring to Applaudo the Following Competencies

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.

Nice-to-Have

Entity resolution, record linkage, or deduplication experience.
Ranking and similarity scoring.
Retrieval, clustering, or candidate-generation techniques.
LLM/embedding solutions designed for cost and scale constraints.
Spark, Snowflake, Databricks, or BigQuery.
Experience with company, domain, website, or firmographic data.
Experience working with multilingual datasets.

You Will Be Accountable for the Following Responsibilities

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.

What Sets You Apart

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.

Additional Information

About Us

We Are Engineered Different.

At Applaudo, talented people design, build, and scale meaningful, AI-powered solutions that create real business impact. As an AI-native organization, we collaborate across design, development, cloud, data, and artificial intelligence to turn ideas into scalable products that transform how companies operate, make decisions, and grow.

We are building a high-performance culture grounded in five values: Empowering Excellence, Collaborative Teamwork, Unsolicited Respect, Consistent Transparency, and Efficient Communication. These define how we work, how we support one another, and how we hold ourselves accountable.

Applaudo is a place for people who want to learn fast, take ownership, and work alongside strong teams they are proud to belong to. Joining us means being part of an organization that is evolving intentionally, investing in modern ways of working, and leading AI-native transformation at scale.