06/01/2026 updated
AK
100 % available
Senior Data Scientist & Machine Learning Engineer
Belgrade, Serbia
Only remote
Bachelor of Computer ScienceArtificial IntelligenceData AnalysisPython (Programming Language)Machine Learning
Machine Learning & Statistical Modeling
Expertise in end-to-end ML lifecycle including statistical modeling, XGBoost, ensemble methods, hypothesis testing, A/B testing, and scikit-learn for building production-ready models.
LLM & Generative AI
Proficiency in building RAG pipelines with LangChain and vector databases, prompt engineering, and designing AI agents using LangGraph for automated workflows.
Data Engineering & ETL on Databricks
Strong background in building ETL pipelines using PySpark, Delta Lake, Kafka, and Databricks, including medallion architecture and Unity Catalog for governance.
Programming Languages
Solid command of Python, R, SQL, and PySpark for data analysis, modeling, and pipeline development.
Cloud & MLOps
Experience deploying models as Docker containers on Azure with CI/CD pipelines via GitHub Actions, as well as using MLflow for experiment tracking and model registry.
Exploratory Data Analysis (EDA)
Hands-on use of Pandas, NumPy, matplotlib, seaborn, and Plotly for automated data profiling, outlier detection, and missing value handling.
A/B Testing & Experimentation
Design and analysis of A/B testing experiments including power analysis, hypothesis testing, causal inference, and monitoring champion/challenger models in production.
Data Visualization & Dashboarding
Creation of automated Plotly dashboards for business stakeholders, enabling self-service data exploration and reporting.
Expertise in end-to-end ML lifecycle including statistical modeling, XGBoost, ensemble methods, hypothesis testing, A/B testing, and scikit-learn for building production-ready models.
LLM & Generative AI
Proficiency in building RAG pipelines with LangChain and vector databases, prompt engineering, and designing AI agents using LangGraph for automated workflows.
Data Engineering & ETL on Databricks
Strong background in building ETL pipelines using PySpark, Delta Lake, Kafka, and Databricks, including medallion architecture and Unity Catalog for governance.
Programming Languages
Solid command of Python, R, SQL, and PySpark for data analysis, modeling, and pipeline development.
Cloud & MLOps
Experience deploying models as Docker containers on Azure with CI/CD pipelines via GitHub Actions, as well as using MLflow for experiment tracking and model registry.
Exploratory Data Analysis (EDA)
Hands-on use of Pandas, NumPy, matplotlib, seaborn, and Plotly for automated data profiling, outlier detection, and missing value handling.
A/B Testing & Experimentation
Design and analysis of A/B testing experiments including power analysis, hypothesis testing, causal inference, and monitoring champion/challenger models in production.
Data Visualization & Dashboarding
Creation of automated Plotly dashboards for business stakeholders, enabling self-service data exploration and reporting.
Experience
Professional experience0 - 5 years
Freelancer since2021
Languages
EnglishFluent
Project history
LLM-powered applications, AI agents, and production ML on Databricks. Built production RAG pipelines using LangChain and vector databases with structured prompt engineering and multi-layer guardrails, reducing hallucination rate by 40%. Designed AI agents with LangGraph for automated customer triage, integrating internal APIs via function calling, reducing manual triage by 60%. Built end-to-end ML pipelines on Databricks using PySpark, Delta Lake, and MLflow with medallion architecture and Unity Catalog for governance. Deployed models as Docker containers on Azure with CI/CD pipelines via GitHub Actions.
Designed demand forecasting system using XGBoost and ensemble methods, achieving 18% MAPE improvement via feature engineering in PySpark on Databricks. Built ETL pipelines in PySpark processing 500M+ daily events into Delta Lake tables with incremental processing reducing runtime by 50%. Conducted EDA using Pandas, NumPy, matplotlib, and seaborn. Designed A/B testing experiments and monitored champion/challenger models in production. Used MLflow for experiment tracking and model registry.
Led A/B testing design and analysis for 10+ features including sample size calculation, randomization, and statistical significance testing. Built XGBoost lead scoring models using scikit-learn with hyperparameter tuning and cross-validation. Performed EDA with Pandas, matplotlib, seaborn, and Plotly. Wrote complex SQL for ad-hoc analysis and automated reporting pipelines.