Applaudo je nearshore podjetje za razvoj programske opreme, osredotočeno na pomoč organizacijam pri posodabljanju tehnologije in napredku digitalne preobrazbe. Podjetje, ustanovljeno leta 2013, združuje strokovno znanje s področij programskega inženirstva in svetovanja na področju inovacij in oblikovanja uporabniške izkušnje, razvoja in vzdrževanja aplikacij, zagotavljanja kakovosti, rešitev v oblaku, umetne inteligence, kibernetske varnosti, podatkovnih rešitev in posodabljanja aplikacij. Applaudo sodeluje s strankami iz sektorjev, kot so šport, mediji in zabava, maloprodaja, široka potrošnja ter finančne storitve, in podpira projekte, ki izboljšujejo digitalne izdelke, infrastrukturo in zagotavljanje IT-storitev. S sedežem v Austinu v Teksasu in pisarno v San Salvadorju Applaudo združuje ekipo od 501 do 1.000 zaposlenih, ki zagotavljajo storitve podjetjem na področju tehnologije in digitalnih storitev.
Senior Applied Data Scientist – Entity Matching (Remote, Peru)
At Applaudo, develop embedding- and LLM-based models to match companies and entities. Evaluate noisy, large-scale data to build accurate AI solutions with practical cost and scale trade-offs.
Opis
- Build and assess machine learning methods for matching companies and entities.
- Create matching approaches with embeddings and large language models.
- Design scoring and ranking methods that separate true matches from duplicates, similar-looking records, and unrelated entities.
- Handle noisy data across names, aliases, domains, websites, firmographic details, languages, and data hierarchies.
- Set up benchmark datasets, evaluation metrics, baselines, and error analysis.
- Plan and run experiments to test hypotheses.
- Measure LLM-assisted methods against more economical options.
- Investigate model behavior, edge cases, and trade-offs.
- Account for inference costs and scalability from the outset.
- Share experimental results and recommendations with engineering and business partners.
- Set up research methods and experiment pipelines independently.
- Record both positive and negative experimental results clearly.
Zahteve
- At least five years of professional experience in data science or machine learning.
- Strong practical foundations in machine learning.
- Excellent Python and SQL skills.
- Practical experience with embeddings and semantic similarity.
- Experience applying large language models to real-world problems.
- Experience with supervised and unsupervised learning.
- Strong background in classification and natural language processing.
- Working knowledge of neural networks and transformer architectures.
- Hands-on use of TensorFlow, PyTorch, PyCaret, or a comparable machine learning framework.
- Experience retraining or maintaining production classification models.
- Strong skills in experimental design and model evaluation.
- Experience setting baselines, metrics, test sets, and error analysis processes.
- Ability to assess model quality and show measurable gains.
- Strong understanding of scalability and machine learning inference costs.
- Strong English communication skills.
- Preferred: Experience in entity resolution, record linkage, or deduplication.
- Preferred: Experience with ranking and similarity scoring.
- Preferred: Familiarity with retrieval, clustering, or candidate generation techniques.
- Preferred: Experience designing LLM or embedding solutions within cost and scale constraints.
- Preferred: Familiarity with Spark, Snowflake, Databricks, or BigQuery.
- Preferred: Experience with company, domain, website, or firmographic data.
- Preferred: Experience working with multilingual datasets.
- Analytical approach and an experimental mindset.
- Intellectual honesty and willingness to share negative results.
- Able to work autonomously and take initiative.
- Excellent written and verbal communication.
- Able to explain and support technical recommendations with stakeholders.
- Strong problem-solving skills.
- Comfortable working with ambiguity and large-scale datasets.
- Able to balance model quality, cost, and scalability.
Ugodnosti
- Remote work option.
- Opportunity to learn quickly and take ownership.
- Work alongside skilled teams.
- Work on modern AI-based solutions and scalable products.
- A high-performance culture centered on excellence, teamwork, respect, transparency, and effective communication.