Grünenthal Group

As Principal Data Scientist, I have been driving the impact initiative "R&D for unmet pain needs" from conceptualisation to active hands-on execution as well as scientifically supervising a team of external  Data Science experts. Within my role, I have led multiple Data Science use-cases across R&D and Medical Affairs:

a) Graph ML-based novel Therapeutic Target Identification in Discovery Biology.
b) Generative AI for de novo drug design in Drug Discovery (Hit-finding, Hit-to-Lead and Lead Optimization)
c) Modelling and Simulation in non-clinical translation Pharmacology for Early Human PK parameter prediction, Early Human Dosing Prediction, Early DILI-risk and CV-risk prediction. 
d) Modelling and Simulation in clinical Pharmacology with Phase I designs (SAD + MAD) for Human Exposure (PK) prediction, PK-PD prediction with SciML and NeuralODEs.  
e) Optimizing Inclusion/Exclusion criteria and Stratification Strategy in Phase II designs (signal detection + dose finding) with ML-enabled Deep Phenotyping. Leveraging (Pharmaco-) genetics datasets, clinical biomarkers, clinical outcomes. 
f) Driving adoption of RWD and Causal Inference methodologies in Clinical Development, Medical and HTA with External Control Arms, Indication Expansion, Treatment Heterogeneity and Evidence Generation use-cases. 
g) Promoting adoption of Digital Health Technology-derived Digital Measures as biomarkers and endpoints in Phase II and Phase III clinical studies.  
h) LLM-based agent applications for internal documents search (RAG), dossier writing (CSR sections, eg. Safety Narratives).

Data Scientist, Associate Director

As the Lead Data Scientist, I was involved in the following pilot R&D projects: 
a) Multi-modal risk model development for sub-population identification in Parkinson Disease with applications in both research biomarker discovery as well as in prognostic enrichment of clinical study populations in Clinical Development.
b) Graph ML model development on large-scale knowledge graphs for drug (and target) safety profile prediction in Research.
c) Causal Inference on RWD for evidence generation in Medical Affairs, HEOR and back-translation to Clinical Development.

Data Scientist, Postdoctoral Researcher

The University of Bonn

I was involved in research projects in Machine Learning for Personalized Medicine applications in Oncology. The work involved:
a) publishing original research methodologies in top ranking journals, and
b) assisting teaching activities and mentoring newer graduate students.

Senior Research Assistant

Universitätsklinikum Bonn

I was analysing brain cancer (GBM) patient data: omics and clinical data. The work involved 
a) running bioinformatics pipelines in the context of BMBF-funded project IDENTIREST. I was responsible for transcriptomics and genetics data analysis with the goal of novel target identification and design of repurposed therapies. 
b) developing Clinical Prediction Models to enable clinical decision making in the form of better diagnosis and prognosis of GBM patients.

Research Assistant

I am an AI/ML expert working in Pharma R&D. In my various roles as a Data Scientist with increasing levels of responsibilities, I have been involved in conceptualising, implementing and scientifically leading AI/ML projects across the Pharma R&D value chain in Discovery Biology, Drug Discovery, Translational Pharmacology, Clinical Development and Medical Affairs. 

I played a central role in Grünenthal's impact initiative "R&D for unmet pain needs" (2021-2025). The initiative's goal was to embed Probability-of-Success improving projects into the R&D pipeline. Before my time in Pharma, I conducted Doctoral work at University of Bonn and University Clinics in Bonn on translational bioinformatics and multi-omics projects in Oncology therapeutic area.

Originally, I am a trained Process Engineer from IIT Bombay, India with an advanced degree in Modelling and Simulation of Industrial Processes from University of Cluj-Napoca, Romania.

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