Lead Data Scientist

This role is for a Lead Data Scientist with a 6+ year IT background, including 3+ years in AI/ML. Requires expertise in deep learning, Python, and frameworks like TensorFlow/PyTorch. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
💰 - Day rate
Unknown
Unknown
🗓️ - Date discovered
January 16, 2025
🕒 - Project duration
Unknown
🏝️ - Location type
Unknown
📄 - Contract type
Unknown
🔒 - Security clearance
Unknown
📍 - Location detailed
Santa Clara County, CA
🧠 - Skills detailed
#NLP (Natural Language Processing) #TensorFlow #Deep Learning #Transformers #Computer Science #AWS (Amazon Web Services) #Supervised Learning #Statistics #GCP (Google Cloud Platform) #Unsupervised Learning #PyTorch #Time Series #Azure #Cloud #Python #Databricks #Programming #AI (Artificial Intelligence) #Reinforcement Learning #Data Science #Neural Networks #ML (Machine Learning) #RNN (Recurrent Neural Networks) #Datasets #"ETL (Extract #Transform #Load)" #Forecasting
Role description
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Job Description:

Mandatory:

● Good GenAI capabilities, with hands-on experience in Tradition ML/deep learning.

● Deep learning expertise is mandatory for a Data science role.

● Candidates should have a good appetite for research and exploration while working.

● should have a fair bit of experience in DL/neural networks or ML. Especially how

CNN, RNN work. Details on How Transformers work. What are the various

components in neural network design? And all optimization algorithms used in

Neural nets.

Qualifications:

● B.E./ B. Tech / M. Tech/ MCA in computer science, artificial intelligence, or a related

field.

● 6+ years of IT experience with a min of 3+ years in Data Science (AI/ML).

● Strong programming skills in Python.

● Experience with Computer Vision.

● Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).

● Hands-on AI/ML modeling experience of complex datasets combined with a strong

understanding of the theoretical foundations of AI/ML(Research Oriented).

● Expertise in most of the following areas: supervised & unsupervised learning, deep

learning, reinforcement learning, federated learning, time series forecasting,

Bayesian statistics, and optimization.

● Hands-on experience on design, and optimizing LLM, natural language processing

(NLP) systems, frameworks, and tools.

● Building RAG applications independently using available open source LLM models.

● Comfortable working in the cloud and high-performance computing environments

(e.g., AWS/Azure/GCP, Databricks).