A
Atharva Joshi
Data Science Intern
Profile
About you
Summary
I am a data scientist with expertise in machine learning and real-time analytics, having built scalable solutions like a traffic management system with a 97% success rate. I excel at leveraging data-driven insights to solve complex problems.
Total work experience
1-3 yrs
I can work legally in
United States
Availability
Immediately
Current work sector
Graduates & Internships(Internships)
Languages
English (US)(Fluent), Hindi(First Language)
New York, New York, United States of America
Education
Postgraduate Degree - Masters of Data Science, University at Buffalo, SUNY
New York, New York, United States of America
Work experience
R&D Intern, Rucha Yantra LLP
2023 - 2024
Data Science Intern, Chandra Engineering
2021 - 2021
Workplace preferences
Ideal workplace
Co-workers
Office meetings
Flexible working hours
Speed of change
Work from home policy
Ideal job
Desired job title
Data Science Intern
Position type
Internships & Graduate Trainee
Travel
50%
Relocation
Yes
Kaggle profile
Followers
none yet
Medals
none yet
none yet Medalsnone yet
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none yet Medalsnone yet
none yet Skills
Personal data
For how many years have you been writing code and/or programming?
3 years
For how many years have you used machine learning methods?
3 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)
- 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
- Do research that advances the state of the art of machine learning
- Experimentation and iteration to improve existing ML models
- Build prototypes to explore applying machine learning to new areas
- Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data
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?
50-249 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
R
SQL
C++
Javascript
MATLAB
Go
CUDA
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
Fast.ai
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
Other
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
Advancements in algorithms
Cloud computing
Data analytics
Development of new software and hardware solutions
Other use cases
Healthcare and Biotechnology
Diagnostic imaging
Protein engineering
Drug discovery
Personalized medicine
Patient data analysis
Predictive modeling for patient care
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
Other use cases
Automotive and Transportation
Development of autonomous vehicles
Traffic management
Route optimization
Predictive maintenance of vehicles
Other use cases
Education
Personalized learning
Predictive analytics for student performance
Educational content recommendation
AI-generated (e.g. LLM) content detection
Cybersecurity
Threat detection
Anomaly detection
Development of secure systems