Staff AI Data Scientist
New
United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonMachine LearningPyTorchTensorflowscikit-learn
Requirements
- Master’s or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
- 3+ years of experience building and deploying machine learning models in production environments.
- Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
- Solid understanding of statistical modeling, experimental design, and evaluation methodologies.
- Experience working with cloud platforms such as AWS, GCP, or Azure for model training and deployment.
- Hands-on experience with LLM fine-tuning techniques (e.g., LoRA, RLHF, instruction tuning) and model serving systems.
- Familiarity with AI-assisted development tools such as GitHub Copilot or similar coding assistants.
- Strong communication skills with the ability to explain complex technical concepts to diverse stakeholders.
Responsibilities
- Design, develop, and deploy machine learning models (supervised, unsupervised, and deep learning) for production-grade use cases.
- Define experimentation strategies, success metrics, and evaluation frameworks including A/B testing, holdouts, and causal analysis.
- Fine-tune, evaluate, and optimize large language models for domain-specific applications and intelligent automation use cases.
- Collaborate with data engineering teams to design and build scalable data pipelines, feature sets, and training datasets.
- Partner with engineering, product, and business stakeholders to translate ambiguous problems into structured modeling solutions.
- Build and maintain model monitoring systems to track performance, drift, bias, and long-term reliability in production.
- Communicate insights, findings, and recommendations through clear reports, dashboards, and data visualizations for technical and non-technical audiences.
- Mentor other data scientists and contribute to improving modeling standards, best practices, and technical rigor across the team.
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