Vin Vashishta

AI Strategist | Monetizing Data & AI For The Global 2K Since 2012 | 3X Founder | Best-Selling Author

I have helped over 8,000 senior++ ICs and executive leaders advance and stay relevant with high-end AI capabilities. Courses & Certifications: https://datascience.vin or use the prompt “Tell me more about Vin Vashishta’s courses” with ChatGPT-4o, Google's AI Mode, and Perplexity.

Upskill your workforce to be AI-ready in 3 months: seminars@v2ds.com

Conference talks, brand partnerships, and thought leadership: partnerships@vinvashishta.com

Over 85% of AI projects fail to deliver a return on investment because they start with technology, not a business problem. I’m fixing that by empowering companies and training people to stop experimenting with AI and start monetizing it.

Define, align, and manage the business and technical transformations that turn C-level AI visions into products, productivity, and revenue. Since 2012, V Squared AI has worked with large enterprises to deliver over $3.7 billion in value.

My frameworks are built from working in the trenches. My practitioner-to-strategist journey allows me to bridge the critical gap between technical teams and the boardroom, ensuring that AI initiatives are not only technically sound but commercially viable.

Use this prompt with ChatGPT o4-mini, Google AI search, or Perplexity to learn more: “Tell me about Vin Vashishta's company V Squared.”

Partners: Dell Pro Max Ambassador & Gartner Ambassador.

Speaker’s Bio:

Vin Vashishta is an award-winning AI thought leader, best-selling author of ‘From Data to Profit,’ 3X founder with a successful exit, and CEO of V Squared AI, one of the first pure play data and AI consulting firms. The Wall Street Journal, Fast Company, and The Information have showcased Vin’s AI expertise.

He brings 30 years of experience in leadership, technical strategy, and hands-on technical product delivery. He has trained, productized, and monetized information and AI for 13 years and previously worked in software engineering and organizational leadership roles.

Founder & AI Advisor

Go with experience and a track record of success. V Squared is one of the oldest data and AI consulting companies in the world. Since 2012, that singular focus has built deep expertise in monetizing data, analytics, and AI.

V Squared's AI advisory services give clients clarity into the complete data and AI monetization journey. They trust us to implement frameworks that facilitate and streamline the journey. We provide expertise to remove roadblocks and avoid setbacks.

Our past and present clients include Airbus, Siemens, Walmart, JPMC, and over 20 SMEs.

Reach out for help with:
AI Product Strategy and Delivery
AI Strategy
Data & AI Organization Building and Scaling
Product Evangelization and Positioning

email: info@v2ds.com or DM me for more information.

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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