Data Scientist / Engineer jobs at Broadcom in United States, Portsmouth
Broadcom Data Scientist / Engineer United States, New Hampshire, Portsmouth
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Job Description:
Data Scientist/Engineer practitioner to work on designing and developing a network operations AI assistant expert system using generative AI, traditional Machine Learning, and statistical analysis tools and techniques.
Classic ML and Statistical Analysis Focused SkillsMachine Learning Skills:
Supervised Learning:Regression (linear, logistic, etc.), classification (SVM, decision trees, random forests, etc.), and model evaluation metrics.
Unsupervised Learning:Clustering (k-means, hierarchical), dimensionality reduction (PCA, t-SNE).
Deep Learning:Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and experience with deep learning frameworks.
Model Selection & Tuning:Cross-validation, hyperparameter optimization, and model selection techniques.
Statistical Analysis & Modeling Skills:
Solid understanding of statistical modeling and machine learning concepts, including:
Time series analysis.
Regression analysis
Classification analysis
Clustering analysis
Hypothesis testing.
Statistical significance.
Bayesian methods.
Programming & Scripting Skills:
Python:Proficient in data manipulation (Pandas, NumPy), data visualization (Matplotlib, Seaborn, Plotly), and machine learning libraries (Scikit-learn, TensorFlow, PyTorch).
R:Experience with data manipulation, statistical analysis, and visualization packages (dplyr, tidyr, ggplot2).
SQL:Ability to query and manipulate large datasets from relational databases.
Cypher/GQL (Optional but beneficial):Ability to query (filter, aggregate, analyze) and manipulate complex graph-based data sets
Data Visualization & Communication Skills:
Creating clear and effective visualizations using various tools.
Communicating complex technical information to both technical and non-technical audiences.
Data storytelling and presentation skills.
Software Engineering Skills:
Programming Languages:Proficiency in Python (essential) and potentially other languages like Java, Scala, or Go.
API Development:Building and consuming APIs to integrate different components of the system.
Version Control (Git):Essential for collaborative development.
Software Design Principles:Ability to design scalable and maintainable systems.
GenAI focused skills
Includes all of the above skills plus these GenAI-specific skills
GenAI Model Integration &Application:
Experience integrating and deploying large language models (LLMs), diffusion models, and other generative AI models into data pipelines and applications. This includes understanding API interactions, prompt engineering, and model limitations.
Data Wrangling & Preprocessing for GenAI:
Expertise in cleaning, transforming, and preparing data for use in generative AI models. This includes handling unstructured data, text normalization, feature engineering specific to generative models, and data augmentation techniques.
Prompt Engineering & Optimization:
Deep understanding of prompt engineering techniques for maximizing the quality and relevance of outputs from generative AI models. Experience with iterative prompt refinement and A/B testing different prompt strategies.
III. AI and Machine Learning Skills:
Large Language Models (LLMs):Deep understanding of how LLMs work and how to effectively integrate them into the RAG system. This includes prompting engineering techniques to obtain desired outputs.
Retrieval Methods:Familiarity with different retrieval techniques, such as BM25, dense retrieval (using embeddings), and hybrid approaches.
Model Selection & Tuning:Ability to choose and fine-tune appropriate models for different tasks within the RAG pipeline.
Explainable AI (XAI):Understanding how to make the system's reasoning transparent and understandable, especially important for building trust in GenAI applications.
Bachelor's + 8+ years of related experience
Compensation and Benefits
The annual base salary range for this position is $107,000 - $171,000
This position is also eligible for a discretionary annual bonus in accordance with relevant plan documents, and equity in accordance with equity plan documents and equity award agreements.
These jobs might be a good fit
Share
Job Description:
Data Scientist/Engineer practitioner to work on designing and developing a network operations AI assistant expert system using generative AI, traditional Machine Learning, and statistical analysis tools and techniques.
Classic ML and Statistical Analysis Focused SkillsMachine Learning Skills:
Supervised Learning:Regression (linear, logistic, etc.), classification (SVM, decision trees, random forests, etc.), and model evaluation metrics.
Unsupervised Learning:Clustering (k-means, hierarchical), dimensionality reduction (PCA, t-SNE).
Deep Learning:Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and experience with deep learning frameworks.
Model Selection & Tuning:Cross-validation, hyperparameter optimization, and model selection techniques.
Statistical Analysis & Modeling Skills:
Solid understanding of statistical modeling and machine learning concepts, including:
Time series analysis.
Regression analysis
Classification analysis
Clustering analysis
Hypothesis testing.
Statistical significance.
Bayesian methods.
Programming & Scripting Skills:
Python:Proficient in data manipulation (Pandas, NumPy), data visualization (Matplotlib, Seaborn, Plotly), and machine learning libraries (Scikit-learn, TensorFlow, PyTorch).
R:Experience with data manipulation, statistical analysis, and visualization packages (dplyr, tidyr, ggplot2).
SQL:Ability to query and manipulate large datasets from relational databases.
Cypher/GQL (Optional but beneficial):Ability to query (filter, aggregate, analyze) and manipulate complex graph-based data sets
Data Visualization & Communication Skills:
Creating clear and effective visualizations using various tools.
Communicating complex technical information to both technical and non-technical audiences.
Data storytelling and presentation skills.
Software Engineering Skills:
Programming Languages:Proficiency in Python (essential) and potentially other languages like Java, Scala, or Go.
API Development:Building and consuming APIs to integrate different components of the system.
Version Control (Git):Essential for collaborative development.
Software Design Principles:Ability to design scalable and maintainable systems.
GenAI focused skills
Includes all of the above skills plus these GenAI-specific skills
GenAI Model Integration &Application:
Experience integrating and deploying large language models (LLMs), diffusion models, and other generative AI models into data pipelines and applications. This includes understanding API interactions, prompt engineering, and model limitations.
Data Wrangling & Preprocessing for GenAI:
Expertise in cleaning, transforming, and preparing data for use in generative AI models. This includes handling unstructured data, text normalization, feature engineering specific to generative models, and data augmentation techniques.
Prompt Engineering & Optimization:
Deep understanding of prompt engineering techniques for maximizing the quality and relevance of outputs from generative AI models. Experience with iterative prompt refinement and A/B testing different prompt strategies.
III. AI and Machine Learning Skills:
Large Language Models (LLMs):Deep understanding of how LLMs work and how to effectively integrate them into the RAG system. This includes prompting engineering techniques to obtain desired outputs.
Retrieval Methods:Familiarity with different retrieval techniques, such as BM25, dense retrieval (using embeddings), and hybrid approaches.
Model Selection & Tuning:Ability to choose and fine-tune appropriate models for different tasks within the RAG pipeline.
Explainable AI (XAI):Understanding how to make the system's reasoning transparent and understandable, especially important for building trust in GenAI applications.
Bachelor's + 8+ years of related experience
Compensation and Benefits
The annual base salary range for this position is $107,000 - $171,000
This position is also eligible for a discretionary annual bonus in accordance with relevant plan documents, and equity in accordance with equity plan documents and equity award agreements.
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