Sr. Data Scientist II
Akaasa Technologies
Raleigh, NC, United States
24 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours
Job source
Tech stack
Amazon Web Services
Amazon Elastic Compute Cloud
Amazon S3
Automation of Tests
Information Leak Prevention
Software Debugging
Linux
Information Extraction
JSON
Python (Programming Language)
Machine Learning
SQL Databases
+11 more
Extensible Markup Language (XML)
Feature Engineering
Pytorch
Prompt Engineering
Generative AI
Git
Semi-structured Data
Scikit Learn
Information Technology
Evaluation of Large Language Models
Tree-based Models
Requirements
- Bachelor’s degree or higher in computer science, Data Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience.
- At least 3 years of relevant experience in data science, machine learning, or NLP, including hands-on experience developing LLM-based applications.
- Strong Python programming skills and experience with SQL and structured or semi-structured data, such as JSON and XML.
- Practical experience with prompt design, structured outputs, retrieval, and systematic LLM evaluation.
- Understanding of statistical evaluation, train/validation/test separation, data leakage, and appropriate performance metrics.
- Experience using Git, debugging existing code, and writing automated tests.
- Ability to communicate findings clearly, incorporate feedback, and proactively raise delivery risks., * Experience with traditional machine learning, including feature engineering, classification, tree-based models, and imbalanced datasets.
- Familiarity with scikit-learn, PyTorch, or comparable frameworks.
- Experience fine-tuning transformer models for classification, information extraction, or sequence labeling.
- Familiarity with AWS, particularly S3 and EC2, and Linux or GPU-based training environments.
- Experience with legal documents, editorial workflows, or other accuracy-sensitive domains.
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