N
Nazina N
data scientist
Profile
About you
Summary
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Total work experience
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I can work legally in
India
Availability
not added
Current work sector
Corporate(N/A)
Languages
English (US)(Fluent), Hindi(Basic)
Kochi, Kerala, India
Education
3 or 4 yr. Undergraduate degree of Computer Science and Engineering
Kollam, Kerala, India
Work experience
Not added
Workplace preferences
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Ideal job
Desired job title
ML Engineer/MLops Engineer
Position type
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Travel
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Relocation
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Kaggle profile
Not added
Skills
Personal data
For how many years have you been writing code and/or programming?
2 years
For how many years have you used machine learning methods?
1 year
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 Engineer
Select any activities that make up an important part of your role at work: (Select all that apply)
- Analyze, understand and visualize data to influence product or business decisions
- Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data
- Build and/or run a machine learning service that operationally improves my product or workflows
- Experimentation and iteration to improve existing ML models
- Do research that advances the state of the art of machine learning
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)?
$1-$99
Approximately how many times have you used a TPU (tensor processing unit)?
2-5 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?
3-4
Technical skills
Generative AI
Prompt Engineering
Embeddings and Vector Stores/Databases
Generative AI Agents
MLOps for Generative AI
Programming languages
Python
SQL
C
C++
Java
Javascript
ML algorithms
Linear or Logistic Regression
Decision Trees or Random Forests
Gradient Boosting Machines (xgboost, lightgbm, etc)
Bayesian Approaches
Evolutionary 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)
Graph Neural Networks
ML frameworks
Scikit-learn
TensorFlow
Keras
PyTorch
Xgboost
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)
Natural Language Processing (NLP) Methods
Transformer language models (GPT-3, BERT, XLnet, etc) - General
Transformer language models - pre-training
Transformer language models - fine-tuning
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
Monitoring
Model testing
Handling of incidents in production
ML OPS best practices
Industry experience
Finance and Banking
Fraud detection
Risk management
Customer service automation
Churn prediction
Other use cases
Technology and Information Services
Cloud computing
Data analytics
Retail and E-commerce
Automated customer service
Recommendation systems
Manufacturing and Industrial Automation
Predictive maintenance
Supply chain optimization
Automation of manufacturing processes
Marketing and Advertising
Sentiment analysis
Market trend analysis
Cybersecurity
Anomaly detection