Company Description

OncoHost is a technology-driven leader in precision medicine, advancing patient outcomes through innovative, clinically validated diagnostic solutions. The company’s proprietary PROphet® platform leverages high-dimensional proteomic pattern analysis to support immunotherapy decision-making for patients with non-small cell lung cancer (NSCLC) using a single blood sample.

Validated in a large-scale, global clinical trial spanning more than 40 sites and 1,700 patients, PROphet® enables physicians to predict treatment efficacy, assess toxicity risk, and uncover biological mechanisms of resistance — supporting more informed, data-driven immuno-oncology decisions.

Role Overview

The Head of Data Science will lead OncoHost’s data science strategy, driving the development of predictive, biologically grounded AI models that support clinical decision-making. This role combines scientific leadership, hands-on technical expertise, and cross-functional influence.

Reporting to the CTO, this leader will shape the long-term data science roadmap, build and mentor a high-performing team, and ensure scientific rigor across all computational activities.

Leadership & Strategy

• Define and execute the company’s data science vision, strategy, and multi-year roadmap
• Build, manage, mentor, and develop a team of Data Scientists, including goal setting, performance evaluation, and professional growth
• Partner closely with senior management and cross-functional teams (bioinformatics, translational medicine, product, scientific affairs, commercial) to drive data-driven decision making
• Establish best practices, methodologies, and quality standards across all data science activities

Technical & Scientific Ownership

• Lead the development and deployment of advanced machine learning and statistical models to predict cancer patients’ response to treatment
• Provide technical leadership and oversight for the analysis of clinical, proteomic, and other multi-omics data
• Guide the design and define workflows for experimentation, validation, and interpretation of supervised and unsupervised learning models
• Ensure model robustness, reproducibility, validation, and statistical rigor

Cross-Functional Impact & Communication

• Collaborate with experts in proteomics, biology and clinical research to translate insights into mechanisms of treatment resistance
• Convert complex analytical outputs into clear, actionable insights for scientific, clinical, product and management stakeholders
• Lead documentation of R&D activities in high-quality technical and scientific reports

Real-World Data & Modeling Environment

• Design and validate ML algorithms with thousands of potential features and small development and validation cohorts
• Identify potential biases in the data (confounding factors, measurement biases, analytic factors, partial data etc.) and proper methods for mitigating them
• Align with restrictive regulation, to allow the utilization of the algorithms for guiding life-saving clinical treatment
• Thrive in a fast-moving, innovation-driven environment with evolving scientific priorities

Required Qualifications

• MSc in quantitative field (Computer Science, Mathematics, Physics, Bioinformatics, or related)
• PhD – an advantage

Core Technical Expertise (Mandatory)

• Proven experience leading or mentoring professional data science teams
• Strong background in Machine Learning and applied Data Science
• Expertise in supervised and unsupervised learning approaches
• Deep understanding of statistics and probability
• Proficiency in Python

Domain Expertise (Advantage)

• LLMs
• Computational biology
• Biology

Personal Attributes

• Strong interpersonal and communication skills, with the ability to operate effectively in cross-functional environments
• Independent, self-driven, and accountable
• Critical thinker with strong problem-solving abilities
• Highly motivated, curious, and able to adapt in a fast-paced setting

Work Model

• Full-time position: 5 days per week
• Hybrid Model: 4 days onsite, 1 day remote