Quantori

Quantori provides comprehensive scientific informatics, data sciences, and digital solutions for the life sciences and healthcare sectors. We serve numerous global pharmaceutical and healthcare clients, offering deep domain expertise with PhD-level skills for AI and technology projects.

VP of Life Sciences Business

Senior Director Life Sciences 

Saleseqn

Providing consulting services to technology SaaS and Consulting organizations. Focused on data-driven approaches to achieving organizational growth.

Founder

Capgemini Engineering

Tessella now part of Capgemini, offers deep expertise in research and development, data science, and engineering delivers rapid business value from your data, whilst helping you progress towards greater digital maturity.  Leading Hybrid Intelligence N. American Life Sciences business. Building strategic partnerships, defining life sciences go-to-market strategy, growing market share, recruiting, mentoring.

Life Sciences AI Sales Director

IDBS

Turned around and led the North American Commercial team to achieve record US growth before and after the acquisition by Danaher. 
Significantly increased US revenue, and built a high-performing commercial team (12 FTE) to support growth plans. 
Defined and executed North American go-to-market strategy. 
Utilized Danaher Business Systems (DBS) tools and processes to achieve growth targets (data-driven approach)

IDBS is a global provider of innovative enterprise data management, analytics and modeling solutions.

Director of North American Sales

Managed IDBS's Strategic Life Sciences accounts (globally). Exceeded revenue targets.

Strategic Account Executive

Tessella

Providing novel informatics solutions and consultancy to the life sciences sector. Tessella excels at both thinking and doing, solving critical business and technology challenges.

Sales Director. Built US Life Sciences business. Defined and led market engagement to increase revenue in the life sciences vertical. Grew revenues through selling specialized, high-technology solutions, and consulting services.

Global Account Director (Life Sciences)

Massachusetts Biotechnology Council

Director of the life sciences informatics committee. Built a steering group of experts and arranged/facilitated Local breakfast meetings and speakers to highlight innovative work in the science/informatics space. 

Mission: To provide meetings where life science informatics professionals can meet one another, build professional networks, and learn more about the challenges and new developments of significance to informatics in the biotechnology and pharmaceutical industries.

Chairman of the Life Sciences Informatics committee

Responsible for developing and managing an internal business development team targetting the pharmaceutical, biotechnology industries, big science, transport, and oil and gas sectors

Sales Executive

SafePharm Laboratories Ltd

Scientist

I operate at the intersection of strategic leadership and hands-on execution. I build, lead, and scale high-performing sales organizations while maintaining direct engagement with enterprise accounts. By combining team leadership with active account management and business development, I drive growth, strengthen client relationships, and position the business for long-term success.

Over the past 20 years, I’ve:

•	Built, led, and mentored high-performing sales teams in AI and SaaS.
•	Managed global accounts and developed go-to-market strategies that drive adoption.
•	Closed multi-million-dollar enterprise engagements and partnerships across R&D industries 
•	Negotiated strategic agreements that expand revenue and accelerate growth.

I’m also the author of Mastering AI Sales: Essential Knowledge for B2B Professionals, a practical playbook that equips founders, consultants, and sales leaders with frameworks to win complex AI sales.
👆 Now available – click the link in my profile header to get your copy.

My mission goes beyond hitting revenue targets. I align advanced technology with business value,  ensuring organizations don’t just adopt AI, but turn it into measurable growth.

Specialties
•	Building & Scaling Sales Teams
•	Enterprise AI Sales & Stakeholder Management
•	Go-to-Market Strategy (Consulting + Product Hybrid Models)
•	Strategic Partnerships & Revenue Expansion
•	Cross-Functional Leadership & Commercial Alignment

Let’s connect if you’re scaling AI solutions, leading enterprise transformations, or looking to accelerate sales impact in complex markets.

David Walker

Everyone says they’re “doing AI in pharma.”
Few actually make it work.

Because AI in life sciences isn’t just about engineers.
It’s about people who understand the science.

At Quantori, our teams blend computational chemists, data scientists, and AI engineers.
They speak the language of R&D, not just code.

That’s why our clients trust us with AI that impacts real science, not just dashboards.

Curious: What’s the biggest gap you see between AI teams and real-world science?

#Quantori #AI #LifeSciencesAI #MLOps #ScientificComputing #PharmaTech

VP of Life Sciences Business | Leading Enterprise AI Partnerships | Team Builder | Author of Mastering AI Sales | MBA, MSc.

I’ve seen too many R&D teams hit the same wall.
They build amazing models...
then run out of compute, speed, or scalability.

That’s where things fall apart.

At Quantori, we built an end-to-end stack that fixes this:
☁️ Cloud HPC for massive simulations
🤖 MLOps for reliable model ops
🧬 AI frameworks tuned for life sciences

From cloudbursting to multimodal AI, we help teams move fast without losing scientific rigor.

Because in science, performance is essential.

#Quantori #HPC #AIInfrastructure #MLOps #LifeSciencesAI #PharmaTech

90% of sellers treat AI like SaaS.
That’s why 90% of them lose the deal.

SaaS = deployment.
AI = data, trust, and change management.

If the data’s messy, the model fails.
If the buyer doesn’t trust it, the deal dies.

AI sales isn’t about features.
It’s about translation, alignment, proof and building trust.

Different beast. Different playbook.

📘 Mastering AI Sales: Essential knowledge for B2B Professionals breaks it all down → https://a.co/d/4e2S2r6

#AISales #AICommercialization #TrustedAdvisor #EnterpriseAI #GenAI

Open article: Mastering AI Sales Essential Knowledge for B2B Professionals by amazon.com

Mastering AI Sales Essential Knowledge for B2B Professionals

The most powerful currency in AI sales isn’t technology, it’s trust.

AI holds massive promise.
But success depends on one thing:
👉 Becoming the trusted advisor your clients rely on for their toughest challenges.

That’s why one of the most important chapters in my new book is:
📘 “Establishing Trust and Credibility in AI Sales.”

Here’s a quick look inside:
🔑 Be transparent: Show your AI’s strengths and its limits.
🎯 Master your client’s world: Translate AI’s real impact.
💡 Share insight: Become a thought leader, not a vendor.
🤝 Build for the long term: Keep trust growing project after project.

Trust isn’t just a value.
It’s the foundation of every successful AI partnership.

👉 Find your copy here: https://a.co/d/2syW6nw

#AI #AISales #TrustedAdvisor #Credibility #SalesLeadership #ClientRelationships #B2BSales #AIStrategy

Everyone wants AI in production.
Few have the discipline to make it happen.

MLOps isn’t optional anymore.

Without it:
🚫 Models sit idle
🚫 DevOps can’t scale science
🚫 No monitoring. No feedback. No progress

This isn’t a tech issue.
It’s an operational maturity issue.

MLOps is how you turn experiments into impact.

At Quantori, we help life science teams move from proof of concept → production with real MLOps discipline.

Because without it...
your AI isn’t working.

#MLOps #AI #PharmaTech #LifeSciencesAI #Quantori

💡 74% of CEOs say they could lose their job within 2 years if they don’t deliver measurable AI results.

That’s from Dataiku’s new Global AI Confessions Report: CEO Edition, and it perfectly captures the shift we’re seeing across life sciences and pharma.

The message is clear:
The era of “AI exploration” is over.
We’ve entered the era of AI accountability, where strategy, governance, and measurable outcomes define success.

Key insights:
🧠 94% of CEOs believe AI agents could soon advise boards as effectively as humans.
🏁 54% admit competitors already have a stronger AI strategy.
🧬 37% have delayed or abandoned AI projects due to regulatory uncertainty.
⚠️ 94% know employees are already using GenAI tools without approval (“shadow AI”).

For pharma and biotech, the implications are huge:

Translating AI from pilots to platforms is now a C-suite priority.

Governance frameworks, scientific, ethical, and regulatory, need to evolve in step with AI innovation.

Those who build trusted AI frameworks and platforms, not just proofs of concept, will define the next generation of R&D productivity.

📘 Full report by Dataiku and The Harris Poll:
👉 https://lnkd.in/eEUw9HJt

#PharmaAI #LifeSciences #AIinR&D #DigitalTransformation #AIAccountability #QuantoriAI

Open article: Global AI Confessions Report: CEO Edition by pages.dataiku.com

Global AI Confessions Report: CEO Edition

Proud to see our vision for agentic AI in Life Science R&D gaining momentum.
 Q-Scientist is redefining how scientists design, test, and learn — faster than ever.
 Details below 👇

Principal and Chief Scientific Officer at Quantori

The future of Life Science R&D is agentic AI.

The buzz at BioTechX Europe this month was incredible! We were honored to present our vision for scientific acceleration with Q-Scientist, our R&D AI/ML platform.

What is agentic AI, and how does Q-Scientist make it possible? It’s about building powerful, flexible workflows using our Multi-Agent Flow Builder. 

This capability lets scientists:
*Design intuitive, powerful workflows without heavy coding.
*Leverage a knowledge library and life science models tailored for genomics,   proteomics, and drug discovery.
*Significantly speed up time-to-insight across the R&D pipeline.

If you missed our talk, "Q-Scientist: A Tailored AI Ecosystem for Life Science R&D," you can download the slides here: https://lnkd.in/e_pV5jCr
Want a deeper dive? Explore Q-Scientist: https://lnkd.in/edxB7XCp

Huge thanks to my co-presenter Artem Terentyuk and the entire Quantori team for a fantastic week of learning and networking!

#Quantori #BioTechX #DrugDiscovery #Genomics #AIAgents #RDAI #BiotechAI

Open article: Building a Trusted AI Hub for a Leading US Healthcare Center | Quantori by quantori.com

Building a Trusted AI Hub for a Leading US Healthcare Center | Quantori

D ONE – Data Driven Value Creation

Life sciences (Alphabet company):
- Architected and built a DWH on GCP integrating external genic knowledge bases
- Developed a RESTful API to interact with the DWH
- Orchestrated jobs for scheduled data ingestion and modelling
- Maintained and implemented new features for multiple cloud-based products
- Enabled large-scale statistical analysis of gene expression profiles
- Cloudified complex data processing pipelines for large transcriptomics datasets with GKE
- Engineered a cloud-based gene pathway representation build system
- Managed and maintained cloud infrastructure as code with templates
- Performed code review and shared best data engineering practices
- Contributed to multiple cloud-based products

Legal-tech (Swiss startup):
- Performed data-driven analytics on two legal cases
- Increased the number of unit tests for key NLP tasks by an order of magnitude
- Increased F1-score for the named entity recognition task by 18%
- Implemented new approaches to the key NLP tasks, such as NER, entity linking and relation extraction
- Prototyped and then developed a new feature, the knowledge graph

Other domains (construction, aviation, consulting):
- Prototyped a staffing web app implementing retrieval augmented generation (RAG)
- Prototyped a GenAI solution on AWS for transportation regulation
- Advised on building an AI assistant on Azure to facilitate construction site assessment

In addition, I organised and led several workshops on Data & AI topics:
- “Building Agile Data Foundations for Data Mesh & Generative AI”,  IEEE Swiss Conference on Data Science 2024
- “Real World Applications of LLMs for Business and Industry”,  IEEE Swiss Conference on Data Science 2024
- “Optimizing Resource Allocation with LLM-based Solutions”,  Growing Together Conference 2024

Senior Consultant

ITMO University

- Supervised 15+ research projects in cheminformatics and AI for materials science
- Co-authored 2 papers for A* conferences (ICML 2024, NeurIPS 2024)
- Co-authored 5 papers for A* conference workshops (AI2ASE, AI4Research)
- Co-authored 5 papers in domain Q1 journals
- Co-authored 1 paper in a Nature Index journal
- Served as a reviewer for NeurIPS, ICLR, ICML, domain Q1 journals
- Established agile methodology for scientific project management
- Established internal and external collaborations
- Contributed to a series of educational courses

Lead Researcher

ETH Zürich

- Developed RALPS (regularized adversarial learning preserving similarity of samples) for multi-batch mass-spectrometry data normalization. It was tested in three application scenarios on two datasets: a newly generated benchmarking dataset and a real clinical one. RALPS outperformed SOTA methods (EigenMS, WaveICA, NormAE, etc.) in all scenarios. A series of ablation experiments has shown the method is quite robust. Published in Bioinformatics.

- Investigated how different AI paradigms compare in learning representations of biological images. I implemented 4 models sharing the same CNN backbone (weakly-supervised, unsupervised, regularized and self-supervised). Models were trained on a big biological dataset under identical conditions. Learned representations were compared in three downstream analysis tasks. Presented at MIDL 2022 (poster) and published in PMLR.

- Systematically investigated utility of pretrained on ImageNet models (ResNet-50, SwAV, DINO, etc.) in analysing a biological dataset of 1.1M drug-treated cell line images. I evaluated models’ performance with multiple metrics on three tasks and compared to the vanilla convolutional autoencoder newly trained on the entire dataset. I made my model publicly available, since it delivered the best overall scores. I further proposed to use distance-based analysis of learned representations to find temporal patterns common for multiple drugs. Presented at MIDL 2022 (oral) and published in PMLR.

- Designed and implemented a monitoring system to keep track of Agilent 6550 iFunnel Q-TOF mass-spectrometer performance (Python + MySQL + R Shiny). The system is composed of three blocks: i) raw signal processing and feature engineering, ii) statistical data analysis, iii) visualisation and reporting through a web-server. It allowed fast and explicit control of key mass-spec properties, as well as potential applications in mass-spec diagnostics and tuning strategies. Published in Frontiers in Molecular Biosciences.

Scientific Assistant

BIOCAD

- Headed up a new systems biology squad , trained the team of 3 associates and built workflows to deliver analysis of pharmacokinetics (PK) and pharmacodynamics (PD) within limited timelines (e.g., PK/PD modelling results, optimal design of clinical trials).

- Established interactions among the group and the other units: departments of preclinical trials, clinical trials and medicine . We integrated PK/PD modelling into a generic product development pipeline, formalised needs and expectations, fixed terms and timelines.

- Managed and supervised PK/PD modeling for various therapeutics of BIOCAD in different development stages. Within 7 months, we successfully delivered reports on modelling of 10 product projects. Those supported decision making and greatly helped interacting with local and European regulators.

- Managed and participated in the development of our internal computational tool for PK/PD modelling (Python). A few cases of PK/PD modelling were solved using this tool.

Systems Biology Team-Lead

- Designed and implemented approaches for modelling of pharmacokinetics and pharmacodynamics (PKPD) of several therapeutics reaching early stages of clinical trials (Python). Based on modelling, I was able to propose dosage regimens ensuring maximal safety and efficacy. These results helped decision making on the projects and were used in reports for local and European regulators.

- Developed a deep learning model to classify 4 breast cancer stages using microscopy images of immunohistochemistry experiments (Python + Tensorflow). The model achieved 95% CV accuracy. 

- Developed a statistical tool for immunogenicity assessment of antibodies (Python backend). I researched and implemented state-of-the-art methods, and also developed my own method that outperformed the others in precision and robustness (on the available data). I wrote documentation and maintained the tool, supporting about 10 researchers from 3 labs who were actively using it. 

- Developed approaches to increase precision of Gibbs energy calculation using OPLS-type force field (Python). I calculated small molecule parameters that were further used in several in-house computational tools (e.g., for protein folding and docking).

Bioinformatics Specialist

Peter the Great St.Petersburg Polytechnic University

Developed a computational tool for agent-based stochastic modelling of gene regulation processes in Drosophila (Java). I implemented several specific mechanisms, such as short-range repression, and simulated dynamics of transcription factors in the vicinity of gap genes. Statistical analysis of multiple simulations and the scientific conclusions were presented on local conferences.

Research Engineer

Applied mathematician by training with a Master in bioinformatics. Doctor of sciences ETH Zurich with work experience in academy, industry and consulting. Presenter and/or speaker at NeurIPS, ICML, MIDL, AMLD, SDS, BioTechX, and other venues. Skilled in data science and engineering, cloud and software engineering. Experienced in research, project and team management. Passionate about AI and its real-world applications. Willing to set ambitious, yet realistic goals and achieve them together.

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