Prospective Causal ML Engineer & Causal Data Scientist | Causal Modeling
15+ years of commercial experience in environmental engineering
5+ years of non-commercial experience in Machine Learning and Data Science
Causality
| Business goal | To make a useful library for future causal projects. |
| Technical goal | Establish methods and metrics of comparing two Directed Acyclic Graphs. |
| Skills learned |
🔹 Measuring how two DAGs can differ. 🔹 Making a lightweight library for future use. |
| Techstack used |
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Machine Learning
| Business goal | Predict how many bikes will be rented. |
| Technical goal | Establish standards for basic MLOps automation. |
| Skills learned |
🔹 Data cleaning and preprocessing using sklearn pipelines 🔹 Project tracking using neptune |
| Techstack used |
   
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Natural Language Processing
| Business goal | Retrieve any interesting common information concerning the citizens' budget dataset |
| Technical goal | Train distributional and statistical NLP models. |
| Skills learned |
🔹 Training an autocorrect model for polish language 🔹 Performing different topic extraction methods 🔹 Training NER models 🔹 Finding similarities in extracted data |
| Techstack used |
       
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| Business goal | Feasibility test for corpus-specific language generation |
| Technical goal | To make a simple autoregressive model in Python. |
| Skills learned |
🔹 Making use of n-grams and sampling methods 🔹 Building a simple typewriter text generation effect |
| Techstack used |
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Agentic AI
| Business goal | Make a POC for automating invoice processing tasks |
| Technical goal | Transfer a primer concept made in GeneratorGPT (as part of the AIdeas challenge) to Python code. |
| Skills learned |
🔹 Ensuring data quality at every step of the process. 🔹 Integrating databases and prior knowledge into Agentic AI task. |
| Techstack used |
 
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