A
Adam Ezzat
Data Science
Profile
About you
Summary
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I can work legally in
United States
Availability
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Current work sector
Technology(Business Analysis)
Languages
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Cupertino, California, United States of America
Education
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Kaggle profile
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Skills
Personal data
For how many years have you been writing code and/or programming?
5 years
For how many years have you used machine learning methods?
5 years
Have you ever published any academic research (papers, preprints, conference proceedings, etc)?
No
Select the title most similar to your current role (or most recent title if retired)
Data Scientist
Select any activities that make up an important part of your role at work: (Select all that apply)
- Experimentation and iteration to improve existing ML models
- Build prototypes to explore applying machine learning to new areas
- Analyze, understand and visualize data to influence product or business decisions
Approximately how much money have you spent on machine learning and/or cloud computing services at home or at work in the past 5 years (approximate $USD)?
$100-$999
Approximately how many times have you used a TPU (tensor processing unit)?
6-25 times
What is the size of the company where you are employed?
0-49 employees
Approximately how many individuals are responsible for data science workloads at your place of business?
1-2
Technical skills
Generative AI
Prompt Engineering
Embeddings and Vector Stores/Databases
Generative AI Agents
MLOps for Generative AI
Programming languages
Python
R
SQL
Rust
Javascript
Bash
MATLAB
ML algorithms
Linear or Logistic Regression
Decision Trees or Random Forests
Gradient Boosting Machines (xgboost, lightgbm, etc)
Bayesian Approaches
Dense Neural Networks (MLPs, etc)
Convolutional Neural Networks
Generative Adversarial Networks
Recurrent Neural Networks
Transformer Networks (BERT, gpt-3, etc)
Autoencoder Networks (DAE, VAE, etc)
ML frameworks
Scikit-learn
TensorFlow
Keras
PyTorch
Xgboost
LightGBM
CatBoost
Caret
Tidymodels
JAX
PyTorch Lightning
Huggingface
Computer Vision Methods
General purpose image/video tools (PIL, cv2, skimage, etc)
Image segmentation methods (U-Net, Mask R-CNN, etc)
Object detection methods (YOLOv6, RetinaNet, etc)
Image classification and other general purpose networks
(VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
(VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
Vision transformer networks (ViT, DeiT, BiT, BEiT, Swin, etc)
Generative Networks (GAN, VAE, etc)
Natural Language Processing (NLP) Methods
Transformer language models (GPT-3, BERT, XLnet, etc) - General
Transformer language models - pre-training
Transformer language models - fine-tuning
Transformer language models - reinforcement learning from human feedback
Text Embedding Models (BGE, E5, T5, etc.)
Production-Grade ML
ML System Architecture design (Training, Inference, microservices orchestration)
Deploying new models to production
AB testing
On-policy vs Off-policy model training
Monitoring
Model testing
Handling of incidents in production
Investigation of production incidents and Root Cause Analysis
ML OPS best practices
Feature store architecture
Latency optimization
Distributed training
Distributed inference
Feedback discussions with end users of the ML systems
Industry experience
Finance and Banking
Algorithmic trading
Fraud detection
Credit scoring
Risk management
Customer service automation
Churn prediction
Other use cases
Technology and Information Services
Cloud computing
Data analytics
Development of new software and hardware solutions
Other use cases
Retail and E-commerce
Personalized shopping experiences
Inventory management
Demand forecasting
Attribution analysis and modelling
Automated customer service
Recommendation systems
Other use cases
Manufacturing and Industrial Automation
Predictive maintenance
Supply chain optimization
Quality control
Automation of manufacturing processes
Automotive and Transportation
Route optimization
Energy and Utilities
Energy demand forecasting
Grid management
Development of efficient renewable energy systems
Marketing and Advertising
Targeted advertising
Customer segmentation
Sentiment analysis
Market trend analysis