Overview
On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Skills
data scientist
Large Language Models (LLMs)
Generative Artificial Intelligence (AI)
Machine Learning (ML)
Artificial Intelligence
Machine Learning Operations (ML Ops)
Python
SQL
Amazon Web Services
Job Details
Looking 12+ years exp. Must be local to San Diego, CA
Position: SENIOR Data Scientist Python, AI, ML
Location: SAN DIEGO CA.
Duration: 6 Months
We are seeking a data scientist who's passionate about the physical world and wants to see their code come to life inthe hum of machinery. You will be our go-to expert for translating high-frequency sensor data into predictive intelligence and for pioneering the use of Generative AI to solve core engineering challenges. You will collaborate shoulder-to-shoulder with mechanical, electrical, combustion and controls engineers to move beyond reactive problem-solving and create a proactive,data-driven manufacturing environment.
Technical Skills:
- Core Programming: You are a master of Python and its scientific computing stack (pandas, numpy, scipy, scikit-learn). Your code is clean, efficient, and production-ready.
- Generative AI & LLMs: You have hands-on experience with large language models (LLMs) and foundation models. You\'re proficient with frameworks like LangChain or LlamaIndex for building RAG (Retrieval-Augmented Generation) systems. Experience fine-tuning models and working with embedding techniques is a major plus.
- Time-Series & ML: You have proven experience with time-series forecasting (e.g., ARIMA, Prophet) and advanced machine learning models (e.g., LSTMs, Gradient Boosting, Isolation Forests). Experience with a major framework like TensorFlow or PyTorch is essential.
- Data Systems: You are fluent in SQL and have hands-on experience with industrial time-series databases and historians like OSIsoft PI, InfluxDB, or similar platforms.
- Cloud & MLOps: You are comfortable working in a cloud environment (AWS preferred) and have experience with MLOps principles versioning data and models, deploying endpoints, and monitoring performance.
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