M
Milosz Goszczynski
Data Scientist
Profile
About you
Summary
cool guy
Total work experience
1-3 yrs
I can work legally in
Poland
Availability
1 week
Current work sector
Technology(Development/Programming)
Languages
English (US)(Fluent), German(Basic), Polish(First Language)
Bydgoszcz, Kujawsko-Pomorskie, Poland
Education
High School of mathematics/informatics , VI Liceum Ogólnokształcące im. JJ Śniadeckich
Bydgoszcz, Kujawsko-Pomorskie, Poland
Work experience
Data Science Internship, Greenfield Capital
2024 - 2024
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
Temporary
Travel
25%
Relocation
No
Kaggle profile
Followers
none yet
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 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)?
$0 ($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?
0-49 employees
Approximately how many individuals are responsible for data science workloads at your place of business?
1-2
Technical skills
Programming languages
Python
SQL
C++
Javascript
ML algorithms
Linear or Logistic Regression
Decision Trees or Random Forests
Gradient Boosting Machines (xgboost, lightgbm, etc)
Bayesian Approaches
Convolutional Neural Networks
ML frameworks
Scikit-learn
TensorFlow
PyTorch
Xgboost
LightGBM
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)
Natural Language Processing (NLP) Methods
Transformer language models (GPT-3, BERT, XLnet, etc) - General
Transformer language models - pre-training
Transformer language models - fine-tuning
Production-Grade ML
ML System Architecture design (Training, Inference, microservices orchestration)
Deploying new models to production
Model testing
Handling of incidents in production
Feedback discussions with end users of the ML systems
Industry experience
Technology and Information Services
Advancements in algorithms
Cloud computing
Data analytics
Development of new software and hardware solutions
Other use cases
Marketing and Advertising
Sentiment analysis
Market trend analysis
Other use cases
Media and Entertainment
Content recommendation
Audience analytics
Real Estate
Property valuation
Market trend analysis
Predictive modeling for real estate investments