P
Parv Bhargava
Data Scientist
Profile
About you
Summary
Data Scientist with deep expertise in NLP, generative AI, time series forecasting, and advanced machine learning. Also, Proficient in SQL, Python, and R. Currently building LLM-based Conversational AI systems and deployable machine learning models.
Total work experience
1-3 yrs
I can work legally in
United States
Availability
3 months
Current work sector
Technology(Development/Programming)
Languages
English (US)(Fluent)
Arlington, Virginia, United States of America
Education
Postgraduate Degree - Masters of Data Science, George Washington University
Washington, District of Columbia, United States of America
3 or 4 yr. Undergraduate degree of Computer Science Engineering , Vellore Institute of Technology
Bhopal, Madhya Pradesh, India
Work experience
Data Scientist, Temporai
2024 - Present
Graduate Assistant, George Washington University
2024 - Present
Student Research Specialist III, George Washington University
2024 - 2024
AI Engineer Intern, MunshiG
2023 - 2023
Associate Software Engineer, Appronic
2022 - 2023
Workplace preferences
Ideal workplace
Co-workers
Office meetings
Flexible working hours
Speed of change
Work from home policy
Ideal job
Desired job title
Data Scientist
Position type
Permanent
Travel
75%
Relocation
Yes
Kaggle profile
Not added
Skills
Personal data
For how many years have you been writing code and/or programming?
6 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 prototypes to explore applying machine learning to new areas
- 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)?
$100-$999
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
R
SQL
C
C++
Java
Bash
CUDA
ML algorithms
Linear or Logistic Regression
Decision Trees or Random Forests
Gradient Boosting Machines (xgboost, lightgbm, etc)
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
LightGBM
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
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
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
Technology and Information Services
Advancements in algorithms
Cloud computing
Data analytics
Retail and E-commerce
Personalized shopping experiences
Inventory management
Demand forecasting
Recommendation systems
Manufacturing and Industrial Automation
Predictive maintenance
Supply chain optimization
Quality control
Automation of manufacturing processes
Automotive and Transportation
Development of autonomous vehicles
Traffic management
Route optimization
Predictive maintenance of vehicles
Telecommunications
Network optimization
Predictive maintenance
Customer service automation
Fraud detection
Agriculture
Crop yield prediction
Soil health monitoring
Optimizing agricultural practices
Education
Personalized learning
Predictive analytics for student performance
Educational content recommendation
AI-generated (e.g. LLM) content detection
Marketing and Advertising
Targeted advertising
Customer segmentation
Sentiment analysis
Market trend analysis
AdTech
CTR / Install Rate prediction
Engagement / LTV Prediction
ROAS prediction & optimization
Bidding strategy optimization
Ad Exchange Optimization
Creative optimization
Cybersecurity
Threat detection
Anomaly detection
Development of secure systems
Media and Entertainment
Content recommendation
Audience analytics
Creation of personalized media experiences
Real Estate
Property valuation
Market trend analysis
Predictive modeling for real estate investments
Logistics and Supply Chain
Route planning
Inventory management
Enhancing the efficiency of logistics networks
Other use cases