P
Pavel Baanerjee
Data Scientist
Profile
About you
Summary
Data-driven professional with over 22 years of experience in customer service and operations. Skilled in customer analytics, inventory management, and process improvement, applying data science methodologies for actionable insights.
Total work experience
15+ yrs
I can work legally in
India
Availability
2 month
Current work sector
Operations(Business management)
Languages
English (US)(Fluent), Bengali(First Language), Hindi(Competent)
Kolkata, West Bengal, India
Education
Certificate/Diploma of Data Analytics, IIT Roorkee
Roorkee
3 or 4 yr. Undergraduate degree of Bachelor in Engineering, Bangalore University
Bangalore
Work experience
Deputy General Manager, Havells India Ltd
2011 - Present
Workplace preferences
Ideal workplace
Co-workers
Office meetings
Flexible working hours
Speed of change
Work from home policy
Ideal job
Desired job title
not added
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?
3 years
For how many years have you used machine learning methods?
2 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
- 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)?
$1000-$9,999
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?
10,000 or more employees
Approximately how many individuals are responsible for data science workloads at your place of business?
5-9
Technical skills
Generative AI
Prompt Engineering
Embeddings and Vector Stores/Databases
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
Convolutional Neural Networks
ML frameworks
Scikit-learn
TensorFlow
Keras
Xgboost
LightGBM
CatBoost
Huggingface
Production-Grade ML
Deploying new models to production
AB testing
Monitoring
Model testing
Handling of incidents in production
Investigation of production incidents and Root Cause Analysis
ML OPS best practices
Feedback discussions with end users of the ML systems
Industry experience
Finance and Banking
Fraud detection
Credit scoring
Customer service automation
Churn prediction
Other use cases
Technology and Information Services
Cloud computing
Data analytics
Other use cases
Healthcare and Biotechnology
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
Other use cases
Telecommunications
Customer service automation
Fraud detection
Agriculture
Crop yield prediction
Soil health monitoring
Optimizing agricultural practices
Education
Predictive analytics for student performance
Educational content recommendation
Marketing and Advertising
Targeted advertising
Customer segmentation
Sentiment analysis
Market trend analysis
Other use cases
Cybersecurity
Threat detection
Government and Public Sector
Urban planning
Public safety
Resource management
Media and Entertainment
Content recommendation
Audience analytics
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