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ML Scientist

$250,000-$280,000

Overview

A fast-growing technology-driven life sciences organization is seeking a highly skilled Machine Learning Research Scientist to help build and advance large-scale foundation models that integrate complex biological data. This role sits at the intersection of artificial intelligence, machine learning research, and scientific discovery.
The successful candidate will work with an interdisciplinary team of researchers, engineers, and domain specialists to develop novel modelling approaches, evaluate emerging technologies, and translate cutting-edge AI research into real-world impact. The position offers significant ownership, opportunities for publication and technical leadership, and exposure to large-scale machine learning systems.

Key Responsibilities

• Design, implement, train, and optimise foundation models that integrate diverse biological and scientific datasets.
• Develop benchmarking frameworks and evaluation methodologies for large-scale machine learning models.
• Assess state-of-the-art open-source models and identify opportunities for improvement or adoption.
• Lead research initiatives from concept through experimentation, analysis, and communication of findings.
• Collaborate closely with technical and scientific stakeholders to translate complex research outcomes into actionable insights.
• Contribute to technical publications, presentations, and engagement with the broader research community.
• Evaluate the use of large language models and related technologies to enhance research workflows and model capabilities.
• Rapidly prototype and test novel ideas, prioritising high-impact experiments and research directions.
• Support the development of scalable machine learning infrastructure for training and deploying advanced models.

Required Qualifications

• Strong publication record and/or presentations at leading machine learning conferences or equivalent research venues.
• Proven experience developing machine learning models and datasets using PyTorch or similar deep learning frameworks.
• Deep understanding of modern machine learning architectures and self-supervised learning techniques.
• Expertise in areas such as representation learning, generative modelling, diffusion methods, latent variable models, or autoregressive architectures.
• Experience training, tuning, and evaluating large-scale machine learning models.
• Familiarity with distributed training environments and scalable machine learning systems.
• Experience with tools and frameworks used for large-scale model development, orchestration, and experimentation.

Preferred Qualifications

• Experience working with multimodal datasets.
• Background in scientific machine learning, computational biology, healthcare AI, or related domains.
• Demonstrated ability to communicate technical research to both technical and non-technical audiences.
• Experience collaborating across disciplines in research-intensive environments.

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Chief Scientist

$300,000- $350,000

Overview

A mission-driven research organisation is seeking a Chief Scientist to lead its scientific strategy, research operations, and interdisciplinary research teams. Reporting directly to the Chief Executive Officer and serving as a member of the executive leadership team, this individual will oversee multiple research functions and ensure alignment between scientific objectives, organisational priorities, and long-term impact.
This role focuses on building and leading a high-performing research organisation rather than serving as an individual contributor. The successful candidate will bring deep expertise in artificial intelligence, substantial leadership experience, and the ability to integrate technical and scientific disciplines into a cohesive research strategy.

Key Responsibilities

Scientific Leadership & Strategy

• Define and oversee a multi-year scientific roadmap aligned with organisational mission and objectives.
• Ensure close collaboration between AI-focused research and domain science teams to drive meaningful scientific outcomes.
• Maintain high standards of scientific rigor, reproducibility, and research integrity.
• Monitor relevant developments in machine learning, computational science, behavioural research, and related disciplines, translating insights into strategic opportunities.

Research Organisation Leadership

• Lead, mentor, and develop senior research leaders and multidisciplinary teams.
• Design organisational structures, staffing plans, and growth strategies to support research priorities.
• Recruit and retain exceptional scientific and technical talent, particularly for senior leadership positions.
• Partner with programme and operational leaders to prioritise initiatives, allocate resources, and ensure effective execution.
• Foster an inclusive, collaborative, and high-performing research culture.

Executive Leadership

• Serve as a strategic partner to executive leadership on organisational direction and long-term planning.
• Align research priorities with fundraising, communications, and organisational initiatives.
• Present research strategy, progress, and key decisions to leadership, funders, boards, and external stakeholders.
• Support external communications by ensuring scientific findings and organisational priorities are accurately represented.

Financial Management

• Own research budget planning and spending within approved parameters.
• Develop and manage multi-year research budget forecasts in partnership with organisational leadership.
• Monitor expenditures and proactively identify significant budgetary changes or risks.

External Engagement

• Represent the organisation at scientific, industry, and professional events.
• Develop partnerships with academic institutions, government organisations, nonprofits, and research collaborators.
• Contribute to publications, thought leadership, and public-facing scientific communications.
• Expand external research networks through fellowships, internships, and academic outreach programmes.

Culture & Ethical Leadership

• Promote cross-functional collaboration and transparent knowledge sharing.
• Create an environment that values respect, inclusion, and diverse perspectives.
• Encourage participation from researchers at all levels in scientific planning and decision-making.
• Uphold strong ethical standards in research design, data usage, interpretation, and communication.

Required Qualifications

Education

• PhD in Machine Learning, Computer Science, Electrical Engineering, Computational Biology, or a closely related discipline.
• Strong scientific credibility and research accomplishments, though an active publication record is not required.

Experience

• 15+ years of experience spanning AI research, large-scale AI implementation, or a combination of both.
• Demonstrated success leading research organisations, technical teams, or advanced AI programmes.
• Significant experience evaluating research quality and managing senior researchers or technical leaders.
• Experience building teams, setting strategic direction, and maintaining scientific standards.
• Strong professional network within the AI research and technology community.
• Ability to recruit and develop senior-level scientific talent.

Technical Expertise

• Deep understanding of modern AI and machine learning methodologies.
• Familiarity with foundation models, large-scale model training, and production-scale AI systems.
• Experience assessing research quality across interdisciplinary environments.
• Understanding of quantitative scientific research methods and data-driven experimentation.

Leadership Skills

• Proven ability to lead complex, multidisciplinary research organisations.
• Strong organisational judgement and strategic decision-making skills.
• Excellent communication skills with executive, scientific, funding, and public audiences.
• Ability to bridge different research cultures and disciplines effectively.
• Passion for mission-oriented scientific research and long-term impact.

Preferred Qualifications

• Experience working in both academic and commercial research environments.
• Background applying AI to scientific or natural-world research domains.
• Familiarity with machine learning for audio, speech, computer vision, or related data modalities.
• Experience in nonprofit, research institute, mission-driven, or field-based scientific organisations.
• Knowledge of responsible AI frameworks and ethical research practices.

Work Model / Reporting Structure

• Reports directly to the Chief Executive Officer.
• Serves as a member of the executive leadership team.
• Oversees senior leaders responsible for research, scientific programmes, and research infrastructure.
• Holds broad authority over scientific direction, research priorities, and organisational research strategy.
• Supported by operational, finance, programme management, legal, and human resources functions.

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Senior Machine Learning Scientist

London (Remote)

The Company
They are an innovative technology business leveraging advanced machine learning, natural language processing, and Generative AI to help organisations extract meaningful insights from large volumes of unstructured data. Their environment combines research, experimentation, and product development, giving technical teams the opportunity to work on real-world AI challenges while delivering measurable business impact.

The Role

• Train, evaluate and optimise machine learning models and agentic AI systems, owning experimentation from end to end.
• Build and enhance LLM-powered solutions including retrieval pipelines, reranking systems, intelligent agents and automated taxonomy generation.
• Design robust evaluation frameworks, define metrics and benchmark model performance across a range of NLP applications.
• Improve semantic search and retrieval capabilities using advanced machine learning and information retrieval techniques.
• Partner with engineering teams to deploy and monitor production-ready machine learning solutions.
• Collaborate with product and business stakeholders to deliver impactful AI solutions while mentoring colleagues and promoting technical best practice.

Your Skills & Experience

• Strong commercial experience with transformer architectures and modern NLP techniques.
• Advanced Python and PyTorch skills, including custom model training and optimisation.
• Experience fine-tuning, evaluating and deploying large language models.
• Experience working with large-scale text datasets and real-world machine learning problems.
• Solid grounding in statistics and classical machine learning.
• Hands-on experience developing Generative AI and agentic AI solutions.
• Excellent communication skills with the ability to engage technical and non-technical stakeholders.

What They Offer

• Competitive salary and benefits package.
• Hybrid working model.
• Opportunity to work on cutting-edge AI and machine learning products.
• Strong career progression within a growing Data Science and AI function.
• Collaborative, research-driven culture with exposure to the latest advancements in machine learning.

How to Apply
To find out more about this Senior Machine Learning Scientist opportunity, please submit your application today.

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Senior Data Scientist

Hybrid – Leicester (1x day a week)

£75,000 – £85,000 + bonus

Looking to apply advanced machine learning in a highly commercial environment? This is an opportunity to join a data-driven organisation where analytics sits at the heart of strategic decision-making. You’ll work on innovative pricing and risk modelling challenges, helping shape how data and machine learning drive business performance.

The Company

They are a well-established consumer-focused organisation with a strong investment in data, analytics, and technology. Their data science function plays a critical role in driving commercial outcomes through advanced modelling and predictive analytics. With a collaborative culture and modern cloud-based technology stack, they offer an environment where innovation and experimentation are encouraged. You will work alongside talented data scientists, analysts, and engineers to deliver impactful solutions at scale.

The Role

As a Senior Data Scientist, you will develop advanced machine learning solutions that support pricing, risk assessment, and business performance.

Responsibilities include:

• Building and deploying predictive machine learning models across a range of commercial use cases
• Developing analytical tools to improve risk portfolio management and decision-making
• Creating innovative features and data assets to enhance model performance
• Identifying and evaluating new data sources that can drive commercial value
• Taking models through the full lifecycle from exploration and development to deployment, monitoring, and optimisation
• Partnering with stakeholders to define business problems and deliver effective solutions
• Working closely with Data Engineers, Data Scientists, Analysts, and other technical specialists
• Contributing to the adoption of emerging machine learning techniques and best practices

Your Skills & Experience

• Strong commercial experience delivering machine learning projects from discovery through to deployment and ongoing model management
• Expertise in Python and SQL
• Experience working with Azure Machine Learning and wider Azure cloud technologies
• Knowledge of version control tools such as Git
• Ability to translate complex business challenges into analytical solutions
• Strong stakeholder management and communication skills
• Experience working in cross-functional data and analytics environments
• Passion for innovation and applying new machine learning approaches to real-world challenges

What They Offer

• Competitive salary
• Annual bonus scheme
• Car allowance
• Private medical insurance
• Matched pension contributions
• Life assurance and income protection
• Hybrid and flexible working arrangements
• Generous annual leave allowance
• Health, wellbeing, and financial support benefits
• Ongoing learning, development, and career progression opportunities within a growing data science function

How to Apply

If you’re interested in this Senior Data Scientist opportunity, apply today to discuss how your experience aligns with the role.

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Staff Data Scientist, Finance – Consumption Forecasting

Location: San Francisco or New York | Hybrid (3 days per week in office)

Salary: $240-300k base + bonus + equity (RSUs)

This is a rare chance to own forecasting infrastructure at the center of a high-growth, consumption-based developer platform, one that powers some of the world’s most dynamic applications and scales with every developer and enterprise building on it.

As a consumption-based business, forecasting usage across compute, bandwidth, edge, and storage isn’t a support function. It’s foundational to how we plan infrastructure, revenue, and long-term strategy. This role exists to lead that work at the highest level.

What you’ll own

This is a senior individual contributor role with organization-wide impact. You’ll define forecasting methodology, build systems that scale with a rapidly growing platform, and sit at the intersection of Finance, Infrastructure, Product, and GTM with direct visibility to executive leadership.

• Own production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, and iteration from first principles to live system
• Build forecasting systems that account for usage-based pricing dynamics, consumption patterns, and customer lifecycle across the platform, built for how this business actually works, not retrofitted SaaS models
• Design hierarchical forecasting models across account, cohort, segment, and global aggregate levels, covering operational, quarterly, and long-range planning cycles
• Establish backtesting, monitoring, and explainability standards that make forecast accuracy transparent and defensible
• Build scenario simulation frameworks to evaluate pricing changes, packaging adjustments, and product launches
• Partner with Finance on board-level reporting, with Infrastructure Engineering on capacity planning, and with Product and GTM on adoption curves and usage drivers
• Set forecasting best practices across the broader Data organization

What we’re looking for

• 7+ years in data science, quantitative analytics, or applied statistics at senior or staff level
• Deep expertise in time-series forecasting and statistical modelling in a usage-based or SaaS environment
• Proven track record building and productionizing ML systems at scale
• Strong Python and SQL, with experience on large-scale usage and billing datasets
• Familiarity with probabilistic modelling, hierarchical forecasting, and causal inference
• Experience partnering with Finance or executive leadership on planning cycles
• Comfortable operating autonomously in fast-moving, ambiguous environments

Nice to have

• Background in cloud infrastructure, developer tools, or consumption-based revenue models
• Familiarity with modern data stacks: Snowflake, Delta Lake, dbt, Airflow
• Prior technical mentorship or informal leadership experience

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Staff GTM Data Scientist

Location: Remote

Salary: $190-220k base

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role sitting at the heart of their product and go-to-market strategy. This isn’t a reporting role or a dashboard-builder position. It’s for someone who can drive a genuine shift toward data-driven decision making across the organization, and who has the technical depth and communication skills to bring leadership along with them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focusing on high-leverage questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to assess the real impact of product and business changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of GTM Data, acting as a strategic thought partner across Product, Marketing, Finance, and Engineering. The company is at an inflection point in how it uses data, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still maturing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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Staff Data Scientist (Product)

Location: Remote

Salary: $190-220k

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role embedded in their product organization. This is for someone who combines deep statistical expertise with the ability to influence how an entire company makes decisions – not just someone who runs experiments, but someone who builds the culture and infrastructure around them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focused on high-leverage product questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to measure the real impact of product changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to meaningful business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of Product Data, acting as a strategic thought partner across Product, Engineering, Finance, Design, and Product Marketing. The company is at an inflection point in how it uses data to make product decisions, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still developing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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