Monte Carlo Simulation Experts in Munich

Philipp G.

Machine Learning & Data Engineer

München

Last position:

Data Scientist & ML Engineer at Data-Science Factory GmbH

Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
Implementation of automated end-to-end cloud processes
Development of LLM and NLP models
Creation of interactive reports
Support for national and international large corporations as well as medium-sized companies in implementing ML projects

Serge K.

Data Science & Machine Learning Operations

Last position:

Data Scientist & MLOps (Machine Learning Operations) at REWE Digital GmbH

It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Development of feature store and data synchronization between environments
Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
Development of Copilot Agent Skills

Raghu Ram V.

Telco Customer Churn Prediction – End-to-End ML Pipeline

Last position:

Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project

Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
Exported reusable pipelines and trained models with joblib for deployment.

Nargiz B.

Risk Manager Consultant

Last position:

Risk Manager Consultant at Friedrich Vorwerk Unternehmensgruppe

Responsible for developing and implementing the Risk Management Policy and Plan, ensuring structured risk identification, assessment, and mitigation
Conducted comprehensive risk assessments on material quantities, procurement schedules, and contractor performance to ensure accurate forecasting and prevent delays
Developed and maintained a risk register covering technical, financial, safety, quality, and environmental risks with focus on quality assurance and quantity control
Collaborated with engineering, procurement, and construction teams to identify deviations in material usage and construction standards, reducing rework incidents by 18% and achieving 10% savings on material costs
Prepared monthly Risk Management Reports highlighting key risks such as supply chain disruptions, cost escalations, and regulatory compliance issues
Led quantitative risk analyses, including Monte Carlo simulations and sensitivity analyses, to evaluate the impact of uncertainties on project timelines and budgets

Borui L.

Spectral Analysis of Neural Network Kernels

Last position:

Spectral Analysis of Neural Network Kernels at Borui Li Projects

Explored the impact of neural network structure on network-inspired kernels, such as Neural Tangent Kernel (NTK).
Demonstrated through theoretical analysis and empirical studies that the RKHS of NNGP is a subspace of NTK.
Explored the connections between these kernels and the Matérn family.

Zakaria M.