Description

You will apply data science and machine learning expertise to solve real-world problems, including record matching, NLP applications, and data quality challenges.

Responsibilities

• Build, validate, and deploy machine learning models for production use cases
• Perform feature engineering and develop approaches to model explainability
• Apply NLP techniques such as sentiment analysis and topic modeling to business workflows
• Implement and validate AI and LLM tooling in real operational contexts
• Design and execute entity resolution and probabilistic record-matching solutions
• Conduct data quality analysis and remediation
• Collaborate with data engineering teams on model productionization and handoff to production systems
• Work with modern cloud data warehouses and data lake environments

Qualifications

• Required
  • Degree in a quantitative field such as data science, statistics, computer science, or applied mathematics, or equivalent professional experience
  • 2–3 years of professional experience using Python and SQL for data analysis, machine learning, NLP, data quality, and record-matching applications
  • Strong proficiency in Python for data science and machine learning, including libraries such as pandas and a modeling stack
  • Proficiency in SQL
  • Demonstrated experience building and validating machine learning models, including feature engineering and model explainability techniques
  • Experience with NLP techniques such as sentiment analysis and topic modeling
  • Experience applying AI and LLM tooling to real workflows with output validation
  • Experience with entity resolution and probabilistic record matching
  • Experience with data quality analysis
  • Familiarity with modern cloud data warehouses and data lakes
  • Ability to collaborate with data engineering teams on production deployment

Key Skills

Education

Any Graduate