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Head of Quantitative Operations

£90,000 – £100,000 + bonus

Remote

This is an opportunity to lead a high-performing quantitative operations function within a well-established organisation at the intersection of research, data, and client services. You will play a key role in shaping operational strategy, driving service excellence, and developing a talented team while working closely with senior stakeholders.

The Company

They are a global organisation that delivers data-driven insights to support strategic decision-making for their clients. With a strong focus on quality, innovation, and operational excellence, they work across complex research programmes and maintain high standards of client delivery. They foster a collaborative environment where leadership, continuous improvement, and professional development are highly valued.

The Role

• Lead the day-to-day operations of the quantitative research and fieldwork function.
• Manage, mentor, and develop a team, ensuring high performance and long-term succession planning.
• Drive operational efficiency, quality standards, and best practice across all projects and processes.
• Oversee resource planning, project allocation, and workload management to optimise delivery.
• Build and maintain strong relationships with internal stakeholders and external clients.
• Ensure projects are delivered on time, to budget, and in line with regulatory and industry standards.
• Support the implementation of strategic initiatives and continuous improvement programmes.
• Monitor team performance, training programmes, and operational KPIs.
• Manage departmental costs and support effective workforce planning.

Your Skills & Experience

• Strong commercial experience leading quantitative operations, research operations, or fieldwork teams.
• Background in market research, healthcare research, data collection, or a related environment.
• Proven ability to manage people, projects, and competing priorities in a fast-paced setting.
• Experience improving operational processes and driving high-quality client delivery.
• Strong stakeholder management and communication skills.
• Knowledge of quantitative research methodologies and project lifecycle management.
• Proficiency with survey and research platforms, alongside advanced Microsoft Excel and broader Microsoft Office tools.
• Analytical mindset with excellent problem-solving and organisational skills.

What They Offer

• Competitive salary and benefits package.
• Opportunity to shape and influence a critical operational function.
• Exposure to senior leadership and strategic business initiatives.
• Career progression within a growing and collaborative environment.
• Ongoing professional development and leadership opportunities.

How to Apply

If you are interested in this Director of Quant Operations opportunity, please apply with your CV to discuss the role in more detail.

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Machine Learning Scientist

Remote (USA only)

About the Role

A frontier AI-driven bio-tech company is hiring a Machine Learning Scientist to help develop next-generation foundation models at the intersection of AI and biology.

This is an individual contributor role for a researcher who enjoys taking ideas from concept to experimentation, working on challenging problems, and collaborating with a highly technical team.

Responsibilities

• Design, train, and evaluate state-of-the-art machine learning and foundation models.
• Rapidly prototype and test new research ideas.
• Develop benchmark tasks and evaluation frameworks.
• Collaborate closely with researchers, scientists, and engineers.
• Contribute to publications and technical presentations.

Ideal Background

• PhD in Computer Science, Machine Learning, AI, Physics, Mathematics, Computational Neuroscience, or a related quantitative field.
• Strong publication record at leading conferences such as NeurIPS, ICML, ICLR, CVPR, or equivalent venues.
• Experience building models in PyTorch and conducting original machine learning research.
• Background in foundation models, LLMs, computer vision, multimodal learning, generative AI, robotics, or scientific machine learning.

Biology experience is a plus but not required. Exceptional researchers from computer vision, language modeling, robotics, autonomous driving, and other highly quantitative fields are encouraged to apply.

Why Join?

• Work on cutting-edge machine learning research with real-world impact.
• Collaborate with world-class researchers and scientists.
• Help shape the future of AI-driven scientific discovery.

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About the Role

This is a high-visibility, high-impact individual contributor role sitting at the intersection of machine learning, clinical data science, and translational biology. You will lead the company’s drug response prediction work – one of the most consequential and technically demanding initiatives in our portfolio.

This is not a pure research role, and it is not a pure engineering role. It requires someone who can move fluidly between rigorous quantitative analysis and the realities of working with large, messy, real-world datasets – someone who can apply state-of-the-art methods without losing sight of what actually works in practice.

The Problem We’re Solving

There are thousands of drugs that work – but only for a small subset of patients, and we largely don’t know why. We are building a systematic engine of understanding between drugs and biology: one that can decode the relationship between a patient’s biology and their response to treatment, and translate that into insights that immediately improve care.

This is foundational, mission-critical work. The person in this role will directly shape how we approach this problem – the methods we use, the data we bring to bear, and the analytical frameworks we build. It is an opportunity to have genuine scientific and clinical impact.

What You Will Do

Drug Response Prediction

• Lead the design and execution of computational approaches to predict drug response across patient populations
• Develop and validate predictive models using real-world clinical data, integrating diverse data modalities
• Identify biological and clinical signals that differentiate responders from non-responders
• Translate modeling outputs into actionable biological and clinical insights

Data & Analysis

• Work extensively with real-world data (RWD) – EHR, claims, clinical trial data – at scale
• Build robust analytical pipelines that handle messy, heterogeneous, and incomplete data
• Apply appropriate statistical frameworks to ensure rigor and reproducibility
• Contribute to the development and evolution of internal data infrastructure and analysis tooling

Modeling & Methods

• Apply and adapt state-of-the-art ML methods – including causal inference, survival analysis, and multi-omics integration – to biological and clinical problems
• Balance methodological sophistication with practical performance constraints
• Evaluate trade-offs between model complexity, interpretability, and real-world utility
• Stay current with the literature and bring relevant advances into the team’s practice

Cross-Functional Collaboration

• Work closely with biologists, clinical scientists, and data engineers to design studies and interpret results
• Communicate findings clearly across both technical and non-technical audiences
• Contribute to a collaborative, intellectually rigorous team culture

What We’re Looking For

Required

• PhD in a quantitative discipline – computational biology, biostatistics, bioinformatics, computer science, physics, statistics, or a related field; open to diverse backgrounds
• Fluent in Python – comfortable writing clean, well-structured code for data analysis and modeling
• Real-world data experience – hands-on work with EHR, claims, or other large-scale clinical datasets
• Ability to work with messy data at scale – experience wrangling, cleaning, and extracting signal from imperfect data
• Strong quantitative intuition – both in modeling design and in interpreting results critically
• Industry experience – 4+ years; 6+ preferred, ideally with both large pharma/biotech and startup exposure
• Mission-driven – genuinely motivated by the opportunity to improve patient outcomes through better science

Strongly Preferred

• Experience with causal inference methods (propensity scoring, instrumental variables, difference-in-differences, etc.)
• Background in statistics, epidemiology, or biostatistics alongside ML
• Familiarity with pharmacogenomics, multi-omics, or translational biology
• Experience contributing to or extending data infrastructure and analysis frameworks
• Track record of working across interdisciplinary teams (biology, chemistry, clinical)
• Startup experience – comfort with ambiguity, ownership, and moving quickly

Background & Experience Profile

We are open to a wide range of PhD backgrounds – what matters most is strong quantitative and analytical foundations, genuine intellectual curiosity, and the ability to work rigorously with complex biological and clinical data. Prior biology experience is not required, but candidates with some exposure to biological or clinical domains will be viewed favorably.

The ideal candidate has spent time in both large pharma/biotech (where they developed rigor and depth) and a startup environment (where they developed speed and ownership). If you haven’t done both, a trajectory that moves toward increasing independence and scope is what we’re looking for.

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ML Scientist / Researcher

Oncology AI · Foundation Models · Life Sciences

Remote

About the Role

We are building foundation models trained on human tumor biology – one of the most consequential and technically demanding challenges at the intersection of AI and medicine. As an ML Scientist, you will be a core research contributor designing and training these models across multimodal omics datasets, partnering closely with biologists and fellow research scientists to advance the state of the art in oncology AI.

This is a research-forward role for scientists who want their work to matter. We are looking for people with a track record of research excellence – those who have gone deep on model architecture, training dynamics, and rigorous experimental design. If you have built models from the ground up and published findings, we want to talk.

What You’ll Do

• Design and train large-scale foundation models on multimodal biological datasets, including genomics, transcriptomics, and other omics modalities
• Collaborate deeply with computational biologists, research scientists, and domain experts to translate biological questions into tractable modeling problems
• Drive the full research lifecycle: hypothesis formation, experimental design, model development, and rigorous analysis of results
• Contribute to agentic AI systems that reason over complex biological data
• Communicate findings internally and, where appropriate, through peer-reviewed publication

What We’re Looking For

Must-Haves

• Strong research background, typically evidenced by a PhD in machine learning, computational biology, statistics, physics, or a related quantitative field – or equivalent industry research experience
• Demonstrated ability to build and train models end-to-end, including experimental analysis and iteration
• Research excellence: first-author publications at top ML, AI, or computational biology venues are a strong positive signal
• Deep familiarity with foundation model concepts: pretraining, self-supervised learning, attention mechanisms, and large-scale training
• Comfort working at the intersection of biology and machine learning – even without a formal biology degree

Nice-to-Haves

• Experience with biological or omics data (genomics, proteomics, pathology imaging, etc.)
• Prior work in multimodal learning or multi-omics integration
• Familiarity with agentic AI systems or tool-use frameworks
• Background in oncology or disease biology

What This Role Is Not

This is not a production ML engineering or MLOps role. We are not looking for candidates whose primary experience is model deployment, serving infrastructure, or engineering-heavy systems work. The emphasis here is firmly on research depth and model development.

Compensation & Location

Base Salary: $250,000 – $288,000 (depending on experience) + equity

Location: Remote-friendly; office in South San Francisco, CA

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Machine Learning Research Scientist

Location: Remote (United States)
Employment Type: Full-Time

Salary: Up to $350K base

The Opportunity

We’re partnering with an innovative, research-driven organization applying cutting-edge machine learning to one of today’s most complex scientific domains.

This is an opportunity for an exceptional Machine Learning Research Scientist to work on novel foundation models, tackling challenging multimodal learning problems at the intersection of modern AI and scientific research.

The role is centred around original research rather than traditional production engineering. You’ll have the freedom to formulate ideas, rapidly test hypotheses, evaluate results, and drive projects from initial concept through to scientific conclusions.

If you enjoy exploring new model architectures, designing rigorous experiments, and pushing the boundaries of machine learning research, this is an opportunity to make a meaningful impact.

What You’ll Do

• Design, implement, and train next-generation foundation models across large, multimodal datasets.
• Develop novel approaches for integrating information from diverse data sources and modalities.
• Design benchmark tasks and evaluation frameworks to measure model performance and scientific value.
• Rapidly prototype new research ideas, designing experiments to validate hypotheses and identify promising directions.
• Collaborate closely with cross-functional researchers, domain experts, and machine learning scientists.
• Evaluate emerging AI techniques, including large language models and agentic AI systems, for research and scientific workflows.
• Communicate research findings to both technical and non-technical audiences.
• Contribute to publications, conference presentations, and broader scientific engagement where appropriate.
• Own research projects from idea generation through experimentation, analysis, and final conclusions.

What We’re Looking For

We’re interested in researchers with a strong academic background and a proven ability to conduct independent machine learning research.

Typical backgrounds include:

• PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, Computational Neuroscience, Physics, or another highly quantitative discipline.
• Strong publication record or evidence of novel research contributions.
• Experience designing and evaluating machine learning experiments from first principles.
• Excellent programming skills with modern ML frameworks such as PyTorch or JAX.

Researchers from a variety of domains are encouraged to apply, including:

• Foundation Models
• Self-Supervised Learning
• Representation Learning
• Computer Vision
• Large Language Models
• Multimodal Machine Learning
• Reinforcement Learning
• Robotics and Autonomous Systems
• Scientific Machine Learning
• Generative AI, Diffusion Models, or Flow Matching

Why Join?

• Work on challenging, research-first machine learning problems.
• Explore novel ideas with genuine scientific ownership.
• Collaborate with world-class researchers across multiple disciplines.
• Contribute to research with the potential for publications and real-world scientific impact.
• Fully remote within the United States.
• Competitive compensation and the opportunity to work on cutting-edge AI research.

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