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обновлено 1 месяц назад

Data Scientist (ML/NLP)

Формат работы
hybrid
Тип работы
fulltime
Грейд
middle/senior
Английский
b2

Описание вакансии

Текст:
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TL;DR

Data Scientist (ML/NLP): Develop and optimize machine learning and natural language processing models to enhance hirify.global's Client Experience Digital Platform with an accent on search value stream problems, user query understanding, and LLM integration. Focus on building scalable, stable, and high-quality data science solutions that deliver clear business value and improve client interactions.

What you will do

  • Collaborate with data engineering, application development, product owners, and business leaders to deliver high-value data science capabilities
  • Solve search-related problems to improve client experience in finding valuable insights
  • Build user query understanding, intent refinement models, and incorporate large language models and vector search
  • Ensure scalability, stability, and business adoption of data science solutions
  • Maintain documentation and promote code reusability
  • Drive long-term vision and strategy in collaboration with senior leadership

Requirements

  • 4-6 years of experience in predictive modeling, search systems, or AI/ML applications
  • Bachelor's degree required; master's preferred in quantitative fields
  • Strong communication skills in technical and business domains
  • Proficiency with Python, ML tools (scikit-learn, spacy, nltk), deep learning frameworks (pytorch, tensorflow), SQL/NoSQL databases, and cloud services (AWS, Azure ML)
  • Ability to translate quantitative analysis into actionable business strategies
  • Experience with Lean product principles and software/ML lifecycle

Culture & Benefits

  • Hybrid work environment with flexibility to work virtually and in vibrant community settings
  • World-class benefits and highly competitive compensation
  • Inclusive teams with diverse backgrounds and cultures
  • Opportunities for professional growth and impactful work
  • Supportive leadership focused on multiplying impact and results