Data-Science/Data-Engineering/AI-Engineering Expert

Responsible for the conception and implementation of AI agent systems deployed on AWS Cloud infrastructure. Maintained continuous alignment with stakeholders throughout the project lifecycle to ensure technical deliverables met business requirements. Collaborated with DevOps teams to establish deployment pipelines and maintain system operations. Technologies: Python/ML, Deno/TypeScript, Streamlit | AWS (Bedrock, Lambda, DynamoDB, S3, EventBridge, SAM) | Git, GitHub Actions (CI/CD)

• POC - Migration MicroService into Kubernetes on GCP
• Creating different manifests for the Kubernetes Cluster
• Performance tests on the Kubernetes Cluster
• Consulting
• Technologies: Git, CI/CD (Gitlab pipelines), Google Cloud, Docker, Kubernetes, PostgreSQL, ElasticSearch

• ETL
• Reporting
• Predictive Modeling (Classification, Regression etc.)
• API Development
• Classification models in predictive behavioral context: FeatureEngineering, ConfusionMatrix, LiftCharts
• Technologies: Git, CI/CD (Gitlab pipelines), PySpark, Pandas, Scikit-learn, Python, SQL, H2o, Docker, FastAPI , TensorFlow, Databricks, PostgreSQL, MongoDB, Terraform, AWS.

• Mentoring.
• End2End data solution
• Developing new customer engagement KPIs.
• Handling complex data structures with SQL
• Conceptualization and implementation:
• Next Best Engagement and marketing cloud automation (E2E).
• Content recommendation with collaborative filtering.
• Impact equivalent optimization with genetic algorithm.
• Technologies: Linux, CI/CD (Gitlab pipelines), Git, Jira, Confluence, AWS, Terraform, Mlflow, SQL, Python Jupyter, PySpark, Pandas, Scikit-learn, Seaborn, Matplotlib, Lightfm.

• Extend a predictive behavioral targeting system with multi-client capabilities (see former tasks in 11/2019).
• Classification models in a predictive behavioral context: FeatureEngineering, ConfusionMatrix, LiftCharts
• Technologies: Linux, Git, Python, PySpark, Pandas, Scikit- Learn, Numpy, Tensorflow/Keras, H2O.ai and MongoDB.

• Conceptual design and develop a machine learning application (predictive analytics) to improve sales distribution in a competitive pharma market.
• Handling complex data structures with SQL.
• Make complex pharma data structures ready for machine learning issues.
• Apply machine learning algorithms, handle smart data, and consult in applied statistics/data science.
• Generate statistical analysis and charts with Jupyter Lab notebooks.
• Classification models in a pharma context: FeatureEngineering, ConfusionMatrix, LiftCharts
• Technologies: Git, Jira, Confluence, Jupyter, SQL, Veeva (CRM), AWS Redshift, Kedro, Mlflow, Python, Jupyter, Pandas, Scikit-Learn, Numpy, Matplotlib, Seaborn, and Plotly.

• Conceptual design and develop a machine learning application to predict online marketing segments in terms of online surf behavior.
• Apply machine learning algorithms, handle big data, consulting in applied data-science specifics.
• Aggregation (ETL) of big data volumes to predict large-scale customer segments to monetize data (DMP/DSP).
• Maintaining and monitoring since 02/2020.
• Technologies: Linux, Git, Jupyter, fastapi, Docker, Apache Spark, Python PySpark, Pandas, Scikit- Learn, Numpy, Tensorflow/Keras, MongoDB, PostgreSQL, H2O.ai, Tensorflow, Pytorch, and Tableau.

• Conceptual design, ETL and develop a machine learning application with a focus on weather effects in terms of sales forecasts.
• Evaluation of AWS Sagemaker in terms of scalability
• Generating large-scale machine learning models and predictions with AWS.
• End2End data solution.
• Regression models in retail context: FeatureEngineering, ModelEvaluation
• Technologies: Linux, Git ,CI/CD (Gitlab pipelines), Jupyter, Docker, Python, Scikit-Learn, Pandas, Tensorflow, Pytorch , AWS Lambda, AWS Glue, AWS S3, AWS Athena, AWS Sagemaker, AWS DynamoDB, AWS DocumentDB, AWS SQS, Serverless.com, and Tableau.

• Building an ETL app to handle big data and generate reports.
• Operations and monitoring since 07/2017.
• Technologies: Linux, Git, Jupyter, PostgreSQL, Python, and AWS.

• TV campaign optimization, media attribution, segmentation, and impact modeling.
• Collaboration and coordination with different stakeholders and partners.
• Handling big data.
• Technologies: MS Office, Linux, PostgreSQL, Spark, R, Python, MongoDB, and Hadoop/Hive.

• Consulting in churn- and predictive analytics.
• Technologies: MS Office, MySQL, R, Python, Weka, MongoDB, and Linux.

• Conceptual design Development of a fingerprint tracking system.
• KPI development.
• Consulting in predictive analytics.
• Handling big data.
• Technologies: MS Office, MySQL, R, Django, Python, Redis, MongoDB, and Linux.

• Segmentation analytics.
• Technologies: SPSS and R.

• Development of an adaptive conjoint design and study execution.
• Server hosting.
• Analytics (market simulation) and result aggregation.
• Technologies: Office, Apache, and Sawtooth.

• Conjoint analytics with cost optimization and server hosting (conjoint survey).
• Technologies: MS Office, Apache, and Sawtooth.

• Database marketing with predictive analytics and online surveys.
• Technologies: MS Office, SPSS, and Weka.

• Consulting in conjoint analytics.
• Technologies: Sawtooth.

• Evaluation of online survey software products.

• Statistical analytics in market research (Conjoint, Customer-Satisfaction and Image surveys, etc.).
• Technologies: SPSS, Sawtooth, R, Excel-VBA, and MS Office.

• Statistical analytics in market research.
• Technologies: SPSS, R, and MS Office.

• Statistical analysis and online research.
• Technologies: SPSS, MS Office.

• Fingerprint Tracking.