- Data projects planning and execution from problem framing to production- Data Pipeline and Data Engineering for KPIs and data monitoring- Data management- Support and maintain prediction algorithm- Statistical analysis for clinical study and scientific experiments- Data analysis and insight generation- Interns mentoringSkills: Advance statistics, Data Science and Analytics, Science, Statistics and Machine Learning, Scientific writing and presentation, Project Management, Mentoring, Visualisation and StorytellingTech Stack: Python, SQL, TypeScript, Google BigQuery, Tableau, AWS DynamoDB

• Consultant for BioPharma company on strategic topics: analysis and delivery directly to clients• Data science project planning, development, and deployment for internal use and for clients• Developed an app in dash for the whole company to retrieve automatically clinical data• Delivered a ML-NLP solution for text-mining leveraging pre-trained models to apply on scientific publication • Introduced Agile methodologies and Industry standards to improve Data Unit productivity and impact (e.g. Jira, Retrospective, Post-mortem)Technical Stack: Python (e.g. pandas, scikit-learn, seaborn), Dash, Spacy, R (e.g. dplyr), Bash, Miro, Jira, OneDrive, Easyretro

Full-stack data scientist: from problem scoping to model deployment in production and monitoringMentoring of mid-level, juniors, and inters across data teamsProject management of multi-disciplinary projectsActively involved in recruitment and in D&I topics across the companyStatistics and modelling, data analysis and visualization, and experimental design support across R&DDelivered a ML and statistical solutions for plant growth optimization: from problem framing to retraining and deployment strategy in GCP. Currently in production

- Planning, designing and developing of machine learning algorithms from conceptualization to deployment- Develop processes to monitor and improve model performance and data accuracy- Problem framing and design of experiments to develop analytics and data-driven decisions- Experimental design and statistical support to stakeholders - Support other data related areas (data engineering, machine learning engineering and analytics) to improve their project quality and performance and deliver high quality integrated systems- Developed and deployed an anomaly detection system for hardware failure- Developed pieces of software to read and act on IoT dataTools:Python (numpy, pandas, scikitlearn, seaborn, statsmodel), GCP (BigQuery, Data Studio, Cloud Functions, Cloud Run, Pub/Sub), R (dplyr, ggplot2, pheatmap, tdyr), SQL, GitHub, Confluence, Jira, User of Docker, Terraform

N2 is a coalition of three doctoral researcher networks of non-university research organisations in Germany – Helmholtz Juniors, Leibniz PhD Networ), and Max Planck PhDnet - that aims to represent and advocate for the collective interests of doctoral researchers at national and international level. - Founding member of the network- Conceptualisation and supervision of the international N² Science Communication conference in the frame of the Berlin Science week 2017- Advocacy of doctoral researchers interests

- Data driven contribution to business decisions (Ad-hoc analyses, KPIs definition and monitoring)- Experiments: design, monitor and evaluation of multiple A/B test- Inventory Risk assesment- Conceptualisation and prototyping of new machine learning products (e.g. NLP-based approach for data matching optimisation)- Direct communication with C-level management and stakeholders- Project management- Directly involved in recruitmentTools: Python (Pandas, statsmodel, numpy, seaborn, scikit-learn), MySQL, Redshift, AWS, Jupyter lab, Git

Study of gene regulation at the enhancer level in embryonic and neuronal differentiated cells with computation approaches.- Establishment and development of an independent computational research project - Delivery of complex scientific results to a broad and diverse audience through compelling data visualisation- Supervision of master students and knowledge transfer to fellow researchers- Peer-revision of scientific papers for high impact-factor journals- Collaboration in the laboratory recruitment process

- Coordination of working groups ( > 30 people)- Strategy development- Representation of Hemholtz Juniors at national level- Responsible Helmholtz Juniors communication- Supervision of the organisation of NextGen@Helmholtz2017 conference

- Teaching to undergraduate students- Responsible for the organisation of training laboratories