About QuantHealth
QuantHealth sits at the center of AI-driven drug development. Our simulation engine runs billions of virtual patient journeys to predict clinical outcomes, streamline trial
design, and lift success rates. Twelve of the top twenty global pharma companies already partner with us, applying the platform across oncology, autoimmune, cardiometabolic, and neuroscience programs. Inside the company, you will work alongside some of the best data scientists, clinical modelers, and software engineers. Your work influences real-world trials measured in billions of dollars and, ultimately, patient lives. Our growth is accelerating - triple-digit ARR, double-digit new partners each year, and a product roadmap funded by top-tier life sciences investors. If you thrive on big challenges, tight-knit teams, and immediate impact, QuantHealth is where you will do the best work of your career.

The Role
The Director of Strategic Partnerships owns new-logo revenue with large biotech and pharma sponsors. You will identify high-value use cases, craft executive-level business cases, and close multi-million-dollar software and license agreements. This is a senior individual contributor role with clear headroom to build a team as growth accelerates.
The annual salary (OTE) for this role is $275,000 - $375,000, depending on experience, skills, and other relevant considerations. 

Responsibilities

• Build and execute account plans for the large biotechs and top 100 pharma
• Create C-suite relationships across clinical development, R&D strategy, and
  digital health functions
• Shape and negotiate complex enterprise agreements
• Qualify and manage pipeline from first hypothesis through signed contract,
  meeting or exceeding quarterly and annual targets
• Partner with Product, Science, and Customer Success to scope pilots, define
  success metrics, and ensure smooth handoff
• Gather market feedback to inform roadmap and go-to-market strategy
• Represent QuantHealthi at industry conferences and advisory boards

Qualifications 

• 7+ years in enterprise sales, business development, or growth strategy,
  including:
  1. At least 5 years with a top-tier management consulting firm
  (McKinsey, Bain, BCG, or equivalent) leading pharma R&D or clinical-
  operations projects 
  2. A mix of consulting and operating roles that demonstrate the same
  toolkit and client-facing rigor
• Direct exposure to pharma clinical development, trial design, or portfolio
  strategy
• Proven record of closing six- and seven-figure deals in complex, multi-
  stakeholder environments in life sciences
• Strong command of AI/ML concepts and the ability to translate technical
  capabilities into business value

Who You Are

• Hypothesis-driven, consultative selling approach
• Ability to lead executive workshops and quantify return on investment
• Skilled at change management and stakeholder alignment across global
  matrixed organizations
• Exceptional written and verbal communication; comfortable with scientific
  discourse and financial modeling
• Operates with urgency, integrity, and a bias toward measurable results
  

Additional Details

• Location: East Coast US preferred; other major pharma hubs considered.
  Regular travel to customer sites and key industry events (≈ 30%).
• Reporting: SVP, Sales with close collaboration with the COO, CEO, Chief
  Medical Officer and VP of Marketing.

About QuantHealth

QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies. Our platform combines large-scale biomedical knowledge, clinical trial data, and patient-level data to help customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success.

About the Role

This role combines day-to-day IT help desk support with ownership of identity and access
governance across the organization. The successful candidate will serve as the primary
point of contact for end-user IT issues while also acting as the gatekeeper for user
provisioning, access control, and compliance with IAM policies.

Responsibilities - IT HELPDESK

· Serve as the first point of contact for IT support requests via ticket, phone, or in
person
· Troubleshoot hardware, software, network, and connectivity issues for end users
· Perform password resets, account unlocks, and MFA resets
· Escalate unresolved issues to second-line support or specialist teams
· Maintain and update the IT helpdesk knowledge base and ticket records
· Set up and configure workstations, laptops, and peripherals for new joiners

Responsibilities - IDENTITY & ACCESS GOVERNANCE

· Create, modify, and deactivate user accounts across Active Directory, Office 365,
and other enterprise platforms
· Manage user provisioning and deprovisioning throughout the employee lifecycle
(onboarding, role changes, offboarding)
· Enforce role-based access control (RBAC) and least-privilege principles across
systems and applications
· Process and review access requests, ensuring approvals align with security policies
· Conduct periodic access reviews and recertification campaigns to identify and
remediate role drift
· Monitor and audit access logs to detect anomalies or policy violations
· Support segregation of duties (SoD) policy enforcement
· Maintain documentation for IAM processes and contribute to policy updates
· Assist with compliance requirements (e.g. ISO 27001, GDPR, SOX) as they relate to
access governance

Qualifications

· 1-3 years in an IT helpdesk or IT support role
· Hands-on experience with Office 365/ Google workspace administration
· Working knowledge of IAM concepts: SSO, MFA, RBAC, LDAP, SAML
· Familiarity with ticketing systems (e.g. Jira Service Management)
· Strong troubleshooting and analytical skills
· Clear communication skills, able to explain technical issues to non-technical users
· High attention to detail and commitment to data accuracy

Nice to have

· Exposure to IGA (Identity Governance & Administration) tools
· Scripting skills in PowerShell or Python for automation tasks

About QuantHealth

QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies. Our platform combines large-scale biomedical knowledge, clinical trial data, and patient-level data to help customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success.

About the Role

As a Data Scientist in the CDS R&D team, you will play a key role in researching, developing, and productionizing advanced data science solutions that power QuantHealth’s clinical simulation and prediction capabilities.

This role is designed for a strong applied data scientist who enjoys solving complex, ambiguous problems end-to-end: from data exploration and feature engineering, through model development and validation, to building scalable pipelines and AI-driven workflows. You will work closely with clinical experts, product, delivery, engineering, and other data science teams to translate challenging clinical and biomedical questions into robust data-driven solutions.

The ideal candidate brings several years of hands-on experience in applied data science, machine learning, and data pipeline development. Additional qualities we look at in candidates are scientific thinking, project ownership, and the ability to independently learn and operate in complex domains. Experience with biomedical or clinical data is a strong advantage.

This position involves working with a modern Python-based data science stack, building predictive models, developing data and ML pipelines, and contributing to the continuous improvement of QuantHealth’s clinical trial simulation product. The role requires curiosity, strong self-learning abilities, research orientation, and rigorous analytical thinking.

Responsibilities

• Design, develop, and validate data science models, algorithms, and tools that support QuantHealth’s internal and customer-facing clinical simulation products.
• Build end-to-end data science solutions, including data ingestion, feature engineering, model development, evaluation, and deployment-ready pipelines.
• Collaborate with clinical, product, delivery, engineering, and data science teams to prototype and implement data-driven solutions.
• Explore, process, and integrate diverse data sources, including clinical trial data, EHR/RWD, biomedical literature, biomedical ontologies, and knowledge graphs.
• Develop scalable ETL and data pipelines to support automated clinical simulation workflows.
• Build LLM-based and agentic systems to automate internal research, data processing, and biomedical reasoning workflows.
• Develop and improve data projects using tools such as PySpark, PyTorch, PyGeometric, MLflow, SQL, and the broader Python data stack.
• Stay current with emerging methodologies in machine learning, LLMs, knowledge graphs, biomedical AI, and applied data science.
• Contribute to internal knowledge sharing, best practices, and scientific discussions within the R&D organization.

Qualifications

• MSc or PhD in data science, computer science, computational biology, or a related quantitative field.
• 4+ years of hands-on experience in applied data science, machine learning, or algorithm development.
• Strong Python skills and practical experience with common data science and ML libraries such as pandas, NumPy, scikit-learn, PyTorch, SQL, and Spark.
• Proven experience building end-to-end data science solutions, from raw data processing to model development, evaluation, and implementation.
• Experience working with large, messy, real-world datasets and transforming them into reliable analytical or machine learning pipelines.
• Strong understanding of machine learning concepts, model evaluation, statistical analysis, and experimental design.
• Experience with cloud-based computing environments and scalable data processing pipelines.
• Strong analytical and problem-solving skills, with attention to details and scientific rigor.
• Strong research mindset and ability to independently learn new domains, methods, and technologies.
• Excellent communication skills and ability to collaborate effectively across clinical, product, engineering, and data science teams.

Strong Advantages

• Experience working with biomedical, healthcare (RWD/EHR), pharmaceutical, biomedical ontologies, or real-world patient data.
• Experience developing LLM-based workflows or agentic pipelines.
• Experience with knowledge graphs, graph embeddings, GNNs, or graph-based ML.
• Experience with PySpark, PyTorch, PyGeometric, MLflow, or similar ML engineering tools.
• Experience creating interactive data applications, dashboards, or internal tools.
• Familiarity with clinical trial design, drug development, computational/system biology, or translational medicine.

About QuantHealth

QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies.

Our platform helps customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success.

About the Role

We are looking for a Senior Data Scientist to lead the development of QuantHealth’s internal platform for streamlining, automating, and standardizing key customer delivery workflows.

This is a hybrid role at the intersection of data science, software engineering, ML systems, and internal product development. The ideal candidate is a strong builder who can take ownership of complex workflows, understand complex real-world data and modeling processes, and turn them into reliable, reusable, scalable systems.

The internal platform will serve as a central layer for improving how customer delivery work is planned, executed, automated, and reused across projects. It will bring together key workflows, automatic agentic systems, data assets, computational processes, reusable components, and quality-control mechanisms into a more integrated and scalable operating model. The goal is to reduce manual effort, improve consistency, shorten delivery timelines, increase transparency, and create reusable institutional knowledge across projects. This role requires someone who is comfortable moving between data science, architecture, software development, data pipelines, AI-driven automation, and internal applications. It also requires collaboration with delivery, clinical, engineering, and data science teams. It is a senior individual contributor role with high ownership and broad impact.

Responsibilities

• Lead the design and development of a set of internal tools that streamline, automate, and scale the company's delivery workflows and model development lifecycle.
• Build data-science-driven systems that combine data pipelines, AI-driven automation, analytical workflows, and internal applications.
• Develop LLM-based and agentic workflows to automate complex operational and analytical processes.
• Implement validation, monitoring, and quality control mechanisms to improve reliability, reproducibility, and trust in delivery outputs.
• Design solutions that support versioning, traceability, access control, and reuse of data and analytical assets.
• Collaborate closely with delivery, clinical, data science, product, and engineering teams to translate operational challenges into scalable technical solutions.
• Own key technical design decisions and contribute to the long-term direction of the internal delivery platform.
• Improve the reliability and maintainability of existing internal tools, workflows, and automation processes.
• Create clear documentation, standards, and best practices for using and extending the system.
• Act as a senior technical contributor, providing guidance and mentorship to junior team members on data science workflows, automation design, code quality, and systems-building best practices.
  
  

Qualifications

• MSc or PhD in computer science, data science, computational biology, or a related quantitative/technical field.
• 6+ years of hands-on experience in applied data science, machine learning, data engineering, or a hybrid data science / systems-building role.
• Strong experience developing complex data science solutions, including data pipelines, automation workflows, model evaluation, and production-oriented implementation.
• Strong Python skills, with the ability to write clean, modular, reliable code for data science and automation workflows.
• Experience working with large, complex, real-world datasets and turning them into reliable data products, pipelines, or analytical workflows.
• Experience integrating data, models, APIs, or internal tools into practical end-to-end solutions.
• Experience with cloud environments, containerization, CI/CD, and production deployment workflows.
• Experience building user-facing automation tools or internal applications that collect user input, guide workflows, and translate user needs into reliable automated processes.
• Strong understanding of machine learning concepts, model evaluation, analytical validation, and experimental design.
• Ability to work with ambiguous requirements, break down complex operational and analytical workflows, and translate them into practical technical solutions.
• Strong product sense for internal tools: ability to understand users, simplify workflows, and build solutions that people actually use.
• Strong analytical and problem-solving skills, with attention to detail, reliability, and reproducibility.
• Excellent communication skills and ability to collaborate across data science, engineering, clinical, product, and delivery teams.
  
  

Strong Advantages

• Experience leading the development of internal platforms, workflow automation systems, data products, or operational tools for technical teams.
• Experience building LLM-based and agentic applications, retrieval systems, or automated reasoning workflows.
• Experience with access control, identity management, audit logs, data lineage, or regulated-data environments.
• Experience working with healthcare, clinical, biomedical, pharma, or real-world patient data.
• Experience working in a startup environment where ownership, speed, and pragmatic engineering are critical

QuantHealth is an AI startup revolutionizing drug development and clinical trials. Our cutting-edge clinical trial simulator can run thousands of trials in parallel to de-risk and optimize real-world clinical trials. Powered by a unique dataset of over 350M patients and 100K drugs, our platform helps drastically reduce drug development costs and timelines. We’re venture-backed, based in Israel, and work with top-tier pharma companies across Europe and the US. 

We are looking for a Senior Back End Engineer (API & Agentic AI) to play a key role in the evolution of QuantHealth’s backend architecture and emerging AI platform capabilities. This role will contribute significantly to the transition from a monolithic application to a multi-service backend platform, while also helping design and implement the backend foundations for agentic AI systems used across our product.

You will work on core backend services, API design, and distributed system concerns, and collaborate closely with full stack, product, data science, and security teams to build scalable, secure, and well-observed services that support both human-facing workflows and autonomous AI agents.

What You’ll Do

Backend & Platform Architecture

• Contribute to the design and implementation of a multi-service backend architecture

• Design, build, and maintain robust, well-documented APIs for internal and external consumers

• Help define backend standards that support both traditional SaaS workflows and AI-driven, agent-based execution

• Collaborate on architectural decisions and provide technical guidance within the engineering team

Agentic AI Enablement

• Design backend primitives and services that support agentic AI systems (e.g., task execution, state management, orchestration, tool invocation)

• Build APIs and infrastructure that allow AI agents to safely interact with internal services, data, and external systems

• Implement guardrails for agent execution, including authorization boundaries, auditability, rate limits, and failure handling

• Partner with data science and ML engineers to productionize AI workflows in a reliable, observable, and secure way

Backend Best Practices & Reliability

• Implement and improve backend best practices, including:

  • Authentication and authorization (token-based auth, RBAC, service-to-service auth)

  • Idempotency and safe retry mechanisms

  • Consistent error modeling and API contracts

  • Input validation and schema enforcement

  • API versioning and backward compatibility

• Ensure backend services follow strong networking and security principles

• Help establish and maintain observability standards (logging, metrics, tracing)

• Write high-quality, well-tested TypeScript code

What We’re Looking For

• 7+ years of professional software engineering experience, primarily focused on backend systems

• Strong expertise in TypeScript for server-side development

• Proven experience designing and implementing APIs for complex systems

• Experience with Nest.js or another major TypeScript backend framework (e.g., AdonisJS, Express, Fastify, Koa)

• Solid understanding of:

  • HTTP and networking fundamentals

  • API security and authentication/authorization patterns

  • Reliability concerns in distributed systems

• Experience with:

  • API documentation (OpenAPI / Swagger)

  • Testing strategies (unit, integration, API tests)

  • Validation and contract-driven development

• A pragmatic, ownership-oriented mindset with strong engineering judgment

• Comfort working in systems that mix deterministic software with probabilistic AI components

Nice to Have

• Experience participating in monolith-to-services or modularization efforts

• Experience supporting external, customer-facing APIs

• Experience designing systems that support AI agents, workflows, or automation platforms

• Familiarity with LLM tooling concepts (tools/functions, agent loops, structured outputs)

• Experience with Effect

• Familiarity with healthcare, life sciences, or regulated environments

• Experience with cloud-native deployments and production observability tooling

Why QuantHealth

• Work on technically deep problems at the intersection of backend systems and AI in healthcare

• Help shape both a scalable backend platform and the foundations of agentic AI capabilities

• Collaborate with a thoughtful, high-caliber engineering team

• Competitive compensation and opportunities for growth