U
Uday Rachapudi
Principal Data Scientist
Profile
About you
Summary
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Total work experience
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I can work legally in
India
Availability
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Current work sector
Retail Banking(Other)
Languages
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Bengalūru, Karnātaka, India
Education
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Work experience
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Workplace preferences
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Ideal job
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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?
12 years
For how many years have you used machine learning methods?
12 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)
Manager (Program, Project, Operations, Executive-level, etc)
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 a machine learning service that operationally improves my product or workflows
- Experimentation and iteration to improve existing ML models
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)?
$0 ($USD)
Approximately how many times have you used a TPU (tensor processing unit)?
Never
What is the size of the company where you are employed?
1000-9,999 employees
Approximately how many individuals are responsible for data science workloads at your place of business?
20+
Technical skills
Generative AI
Prompt Engineering
Generative AI Agents
MLOps for Generative AI
Programming languages
Python
R
SQL
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
ML frameworks
Scikit-learn
TensorFlow
PyTorch
Xgboost
Natural Language Processing (NLP) Methods
Transformer language models (GPT-3, BERT, XLnet, etc) - General
Transformer language models - fine-tuning
Transformer language models - reinforcement learning from human feedback
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
Feedback discussions with end users of the ML systems
Other
Industry experience
Finance and Banking
Fraud detection
Credit scoring
Customer service automation
Churn prediction
Technology and Information Services
Data analytics
Retail and E-commerce
Personalized shopping experiences
Demand forecasting
Attribution analysis and modelling
Recommendation systems
Marketing and Advertising
Targeted advertising
Customer segmentation
Sentiment analysis
Market trend analysis