Applaudo tashkilotlarga texnologiyalarini modernizatsiya qilish va raqamli transformatsiyasini rivojlantirishda yordam berishga ixtisoslashgan nearshore dasturiy ta’minot ishlab chiqish kompaniyasidir. 2013-yilda tashkil etilgan kompaniya innovatsiyalar va tajriba dizayni, ilovalarni ishlab chiqish va qo‘llab-quvvatlash, sifatni ta’minlash, bulutli yechimlar, sun’iy intellekt, kiberxavfsizlik, ma’lumotlar yechimlari hamda ilovalarni modernizatsiya qilish yo‘nalishlarida dasturiy ta’minot muhandisligi va konsalting tajribasini birlashtiradi. Applaudo sport, media va ko‘ngilochar industriya, chakana savdo, kundalik iste’mol tovarlari hamda moliyaviy xizmatlar kabi sohalardagi mijozlarga xizmat ko‘rsatib, raqamli mahsulotlar, infratuzilma va IT xizmatlarini yetkazib berishni yaxshilashga qaratilgan loyihalarni qo‘llab-quvvatlaydi. Bosh qarorgohi Texas shtatining Ostin shahrida, ofisi esa San-Salvador shahrida joylashgan Applaudo texnologiyalar va raqamli xizmatlar sohasidagi bizneslarga xizmat ko‘rsatadigan 501–1 000 nafar xodimdan iborat jamoani birlashtiradi.
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.
Tavsif
- 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.
Talablar
- 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.
Imtiyozlar
- 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.