Sr. Applied Data Scientist

Applaudo Studios

About The Position

Applaudo is seeking 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. The role involves working with embeddings, semantic similarity, LLMs, NLP, classification, and supervised and unsupervised learning. The successful candidate will approach ambiguous problems through structured experimentation, clearly defined hypotheses, baselines, metrics, and error analysis; work autonomously; communicate experimental results honestly; and explain technical recommendations and trade-offs to engineering and business stakeholders. The position focuses on building and evaluating machine-learning approaches for company and entity matching. This includes developing embedding- and LLM-based matching approaches, designing scoring and ranking methodologies, and working with noisy, multilingual, and hierarchical company data. The role also involves establishing benchmark datasets and experimental pipelines, comparing LLM-assisted methods with lower-cost alternatives, documenting results, and accounting for inference economics and scalability. Applaudo describes itself as an AI-native organization where teams across design, development, cloud, data, and artificial intelligence build and scale AI-powered solutions. Its culture emphasizes Empowering Excellence, Collaborative Teamwork, Unsolicited Respect, Consistent Transparency, and Efficient Communication.

Requirements

At least 5 years of professional Data Science or 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.
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.

Nice To Haves

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

Responsibilities

Build and evaluate machine-learning approaches for company and 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 successful and unsuccessful experiments.