data & AI
Diversity Report

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Senior Data Engineer / Analytics Engineer
Downtown San Francisco | Hybrid (4 days onsite)

What You’ll Do

Data Engineering & Infrastructure

• Design, build, and maintain data pipelines from ingestion through transformation and delivery
• Own data modeling and transformation across the firm’s core data sources
• Build and maintain a reliable, scalable data infrastructure from the ground up
• Ensure data quality, consistency, and governance across systems

Analytics & Business Partnership

• Serve as the primary technical translator between data systems and non-technical business stakeholders
• Partner with wealth management, finance, operations, and leadership teams to understand data needs and deliver solutions they can actually use
• Build reporting and analytics capabilities that give the business clear visibility into performance and client data
• Communicate complex data concepts in plain language – confidently and clearly

AI & Modern Tooling

• Leverage AI tools to improve productivity and analytical output – familiarity with tools like Glean expected
• Contribute to the firm’s evolving AI initiatives as they develop
• Stay current on relevant AI and data tooling and bring practical recommendations to the team

What We’re Looking For

Required

• Strong proficiency in Python and SQL
• Hands-on experience with data modeling, transformation, and pipeline development
• Ability to work independently as the primary or sole data practitioner – this is not a large team environment
• Confident communicator – comfortable presenting to and advising non-technical stakeholders including senior leadership
• High EQ – personable, collaborative, and able to build trust with people across the organization
• 4 days per week onsite availability in downtown San Francisco

Strongly Preferred

• Background in finance, wealth management, or financial services
• Experience at a private equity-backed company or PE rollup environment
• Familiarity with wealth management workflows, data structures, and reporting needs
• Experience as the sole or primary data person at a company – someone who has had to be resourceful, self-directed, and scrappy
• Exposure to AI tools in a professional context (Glean or similar)

Compensation

• Base salary: $185,000 – $235,000
• Discretionary bonus
• Downtown San Francisco office – hybrid, 4 days onsite per week

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This is an exciting opportunity to shape the future of CRM strategy within a growing research and insights business. Sitting at the centre of Sales, Marketing and Operations, you will combine strategic thinking with hands-on execution to drive adoption, efficiency and commercial performance.

Role:
They are a fast-growing data, research and analytics organisation serving a global client base. Their insights help businesses make informed commercial decisions across a range of industries.

As CRM Manager, you will be responsible for owning and developing the company’s internal CRM system, with a primary focus on HubSpot, which was implemented approximately six months ago.

Responsibilities include:

• Leading the CRM strategy and roadmap across sales, marketing and operational teams
• Acting as the internal HubSpot expert, driving adoption and best practice across the business
• Maintaining, enhancing and optimising HubSpot Sales Enterprise and Marketing Enterprise
• Building reporting frameworks and dashboards to provide meaningful commercial insights
• Developing and managing automation workflows to improve efficiency and reduce manual processes
• Working closely with stakeholders across Sales, Marketing, Operations and Finance to identify opportunities for further CRM development
• Ensuring data quality, governance and accurate reporting across the CRM platform
• Delivering HubSpot training and ongoing support to users across the organisation
• Managing integrations and exploring future platform enhancements to support business objectives

Your Skills & Experience

• Strong commercial experience managing and developing HubSpot CRM environments
• Experience owning CRM strategy while remaining hands-on with platform administration and optimisation
• HubSpot Marketing and Sales certification, or equivalent practical expertise
• Experience building automations, workflows, reporting and dashboards within HubSpot
• Knowledge of HubSpot
• Ability to work cross-functionally and influence stakeholders across multiple business areas
• Strong analytical skills with the ability to translate data into actionable business recommendations

How to Apply
To find out more about this CRM Manager opportunity, please submit your application today.

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Data Analyst – Up to 45k

This is an opportunity to work with a genuinely unique behavioural dataset, combining digital activity, consumer journeys and emerging AI interactions. You will help answer complex commercial questions for leading brands while shaping how data is used to understand real-world behaviour.

The Company
They are a specialist data and analytics business focused on understanding how advertising and digital experiences influence consumer behaviour. Their work blends advanced analytical techniques with rich, multi-source datasets to uncover insights that traditional approaches cannot provide. Operating as a close-knit team, they encourage new ideas and give individuals the autonomy to make a real impact. Their client base includes well-known organisations across media and consumer sectors.

The Role

You will work hands-on with large behavioural datasets, analysing how consumers interact across digital platforms, media channels and AI tools. Responsibilities include:

* Extracting, querying and analysing data using SQL
* Cleaning and structuring complex datasets for analysis
* Identifying patterns, trends and commercially relevant insights
* Delivering post-campaign analysis to evaluate performance
* Supporting bespoke analytical projects addressing business challenges
* Translating data into clear, client-ready insights and presentations
* Working across both quantitative and qualitative data sources
* Developing scalable analytical processes and methodologies
* Collaborating closely with senior stakeholders to evolve analytical outputs

Your Skills & Experience
* Strong commercial experience working in a hands-on data analysis role
* Advanced SQL skills for data extraction and manipulation
* Confidence working with large, complex datasets
* Strong Excel capability for analysis and data handling
* Ability to interpret data and communicate insights effectively
* Experience with quantitative analysis and problem-solving
* Curiosity and attention to detail when exploring new datasets
* Familiarity with modern AI or LLM tools and an interest in their application within analytics

What They Offer
* Salary up to £45,000
* Hybrid working model with regular collaboration in a London office
* Exposure to highly unique and innovative datasets
* Opportunity to work closely with senior leadership
* A fast-paced environment with strong ownership and career development potential

How to Apply
If you are a commercially minded Data Analyst looking to work with cutting-edge datasets, apply to find out more.

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Overview

A large, complex enterprise is seeking a senior data science leader to build and scale its data science function. This role is responsible for translating data into predictive, prescriptive, and AI-enabled insights that improve customer experiences, operational performance, decision-making, and organisational outcomes.
The Director, Data Science will lead teams focused on statistical modelling, machine learning, experimentation, forecasting, optimisation, decision support, and AI-enabled analytics. The position partners closely with data, engineering, product, digital, and business stakeholders to ensure analytical solutions are built on trusted data and deliver measurable value.
This leader will establish best practices for model development, validation, deployment readiness, monitoring, and lifecycle management while fostering a high-performance, innovation-focused culture.

Key Responsibilities

Data Science Strategy & Leadership

• Define and execute the enterprise data science roadmap aligned to strategic business priorities.
• Identify opportunities where advanced analytics, machine learning, optimisation, and AI can improve organisational outcomes.
• Prioritise investments based on business value, feasibility, data readiness, and strategic importance.
• Establish standards for experimentation, model development, validation, documentation, monitoring, and governance.
• Ensure solutions are scalable, reusable, interpretable where appropriate, and aligned with enterprise architecture and responsible AI principles.

Advanced Analytics, Machine Learning & AI

• Lead development of predictive, prescriptive, optimisation, and forecasting models across multiple business domains.
• Oversee customer segmentation, risk modelling, recommendations, personalisation, propensity modelling, and decision-support solutions.
• Partner with engineering and product teams to operationalise analytical capabilities within production environments and business workflows.
• Support AI initiatives by defining methodologies, evaluation approaches, and performance expectations.
• Promote modern statistical and machine learning techniques while ensuring appropriate application and risk management.

Model Evaluation, Experimentation & Measurement

• Define frameworks to evaluate model performance, business impact, reliability, fairness, and adoption.
• Lead experimentation strategies including A/B testing, causal inference, impact measurement, and test design.
• Collaborate with engineering, product, quality, and business teams on model monitoring and continuous improvement.
• Translate analytical outputs into actionable insights and business recommendations.
• Establish reporting, dashboards, and executive-level performance reviews.

Cross-Functional Collaboration

• Partner with enterprise data teams to ensure access to reliable, high-quality data.
• Collaborate with knowledge management, semantic modelling, and data product teams to improve analytical outcomes.
• Work closely with product, operations, technology, and business leaders to identify and prioritise analytical opportunities.
• Support production deployment of models and analytical services in partnership with engineering teams.
• Provide strategic guidance on where advanced analytics and AI can create value, and where alternative approaches may be more appropriate.

Team Leadership & Development

• Build, lead, and mentor a high-performing team of data scientists and analytics professionals.
• Define organisational structure, hiring strategy, capability development, and career progression pathways.
• Establish effective collaboration models across data, engineering, governance, product, and business teams.
• Coach team members on technical excellence, stakeholder engagement, communication, and business impact.
• Foster a culture of innovation, accountability, continuous learning, and responsible AI.

Required Qualifications

Education

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, Operations Research, Public Health, Informatics, or a related field.
• Master’s degree or PhD preferred.

Experience

• 12-18+ years of experience in data science, machine learning, advanced analytics, applied statistics, or related disciplines.
• 5-8+ years leading data science, analytics, or machine learning teams.
• Proven experience delivering analytical solutions that influence business or operational decision-making.
• Experience partnering with engineering, technology, product, and executive stakeholders.
• Strong background in model development, experimentation, monitoring, and production deployment.
• Experience within a regulated industry preferred.

Technical Expertise

• Deep expertise in statistical modelling, machine learning, predictive analytics, optimisation, experimentation, and decision science.
• Proficiency with Python, R, SQL, notebook-based development environments, and modern machine learning frameworks.
• Experience assessing data quality, feature readiness, model performance, bias, drift, interpretability, and operational fit.
• Understanding of model deployment, monitoring, integration, and lifecycle management within production environments.
• Familiarity with AI, Generative AI, large language model evaluation, recommendation systems, forecasting, personalisation, and decision-support methodologies.
• Strong knowledge of privacy, security, governance, and responsible AI practices.

Preferred Qualifications

• Experience leading data science teams within healthcare, life sciences, financial services, or other highly regulated industries.
• Experience supporting AI-enabled products, digital experiences, operational optimisation, workflow automation, or decision-support capabilities.
• Experience building reusable analytical frameworks and platforms that support multiple business functions.
• Experience defining enterprise standards for model evaluation, observability, monitoring, and continuous improvement.
• Familiarity with semantic models, ontologies, knowledge graphs, or enterprise knowledge management frameworks.
• Strong executive communication and stakeholder management skills.

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Senior Analytics Manager

Permanent | £100,000 Base + Benefits
Central London | Hybrid (3 Days Per Week)

The Role:

A leading organisation is seeking a Senior Analytics Manager to drive analytics strategy, deliver actionable insights, and support business-wide decision making.

Responsibilities:

• Lead analytics initiatives that deliver measurable business value.
• Partner with senior stakeholders to define KPIs and reporting requirements.
• Drive adoption of data-driven decision making across the organisation.
• Oversee the delivery of dashboards, reporting, and analytical products.
• Promote best practice across analytics, governance, and self-service reporting.
• Mentor and develop members of the analytics team.

Requirements:

• Proven experience in Analytics, BI, Data & Insights, or Analytics Leadership roles.
• Strong SQL and DBT experience.
• Experience with modern BI and reporting platforms.
• Excellent stakeholder management and communication skills.
• Ability to translate business challenges into analytical solutions.
• Experience leading cross-functional analytics projects.

What Will Make You Stand Out:

• Experience with cloud data platforms
• Exposure to AI and advanced analytics use cases.
• Strong understanding of data modelling and metric governance.
• Track record of driving analytics transformation programmes.
• Experience developing and mentoring analytics teams.

Package:

• £100,000 Base Salary
• Comprehensive Benefits Package
• Permanent Position
• Hybrid Working (3 Days Per Week In The Office)
• Central London Office
• Opportunity to shape analytics strategy and influence senior business decisions

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Chief AI Scientist

US Remote

$320,000-340,000 Base Salary + Benefits

THE COMPANY

Our client is a mission-driven nonprofit research organization operating at the intersection of artificial intelligence, natural sciences, and emerging technologies. The organization is pioneering the application of advanced AI and machine learning techniques to better understand communication and behavior in the natural world.

Working with leading researchers, academic institutions, and global partners, the organization is building cutting-edge research programs that combine foundation models, large-scale scientific datasets, and interdisciplinary expertise. This is a rare opportunity to join a purpose-led organization tackling one of the most ambitious scientific challenges of our time.

RESPONSIBILITIES

• Lead the organization’s long-term scientific strategy across AI, machine learning, and interdisciplinary research programs.
• Build, develop, and oversee a high-performing research organization, providing leadership to senior research leaders and their teams.
• Ensure strong integration between AI research initiatives and domain-specific scientific research, creating a cohesive and impactful research agenda.
• Partner with executive leadership to align research priorities with organizational goals, funding strategies, and long-term mission objectives.
• Recruit, mentor, and retain exceptional research talent while shaping organizational design, team structures, and capability development.
• Represent the organization externally through scientific publications, conferences, industry engagement, partnerships, and thought leadership activities.
• Oversee research budgets, resource allocation, and strategic investment decisions to maximize scientific impact.
• Foster a culture of scientific rigor, reproducibility, ethical stewardship, collaboration, and innovation.

SKILLS AND EXPERIENCE

Required

• PhD in Machine Learning, Computer Science, Electrical Engineering, Computational Biology, or a closely related discipline.
• 15+ years of experience spanning AI research, large-scale AI implementation, or a combination of both.
• Proven leadership experience managing senior researchers and directing complex research organizations.
• Deep understanding of modern AI and machine learning, including foundation models, large-scale training systems, and applied research methodologies.
• Strong ability to assess research quality, set technical direction, and provide strategic oversight to advanced research teams.
• Established professional network within the AI and machine learning community with a demonstrable track record of attracting top talent.
• Exceptional communication skills, with experience engaging executive stakeholders, boards, funders, research communities, and external partners.

Preferred

• Experience applying AI to scientific or natural science research domains.
• Background spanning both academic and commercial research environments.
• Expertise in audio, speech, computer vision, bioacoustics, or related machine learning disciplines.
• Experience working within nonprofit, mission-driven, research-intensive, or field-science organizations.
• Familiarity with AI ethics, responsible AI frameworks, and research governance.

BENEFITS

• Executive-level leadership opportunity with significant scientific and organizational influence.
• Opportunity to shape an ambitious, globally relevant research agenda at the frontier of AI and science.
• Collaboration with leading researchers, academics, and strategic partners.
• Flexible working arrangements.
• Competitive executive compensation package commensurate with experience.

HOW TO APPLY

Please register your interest by sending your CV via the Apply link on this page.

KEY TERMS

Chief Scientist | AI Research Leadership | Executive Leadership | Machine Learning | Foundation Models | Research Strategy | Artificial Intelligence | Scientific Leadership | Applied AI | Deep Learning | Research Management | Computational Biology | Bioacoustics | Natural Sciences | Interdisciplinary Research | Nonprofit Research | AI Infrastructure | Research Operations | Executive Team | Talent Development | Responsible AI | Scientific Innovation | Audio | Speech | Mulitmodal | Multi modal | Multimodality | Multi-agent | Agentic AI | Generative AI | Non-profit | NGO | Executive

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Lead ML Platform Engineer

New York City, NY – Hybrid (3 days per week onsite)

$200,000 – $220,000 Base Salary+ Bonus + RSU package available

THE COMPANY

We are partnering with a leading consumer technology and financial services organization that operates at global scale and serves hundreds of millions of users. The business leverages advanced data, machine learning, and real-time decisioning systems to deliver highly personalized customer experiences across a broad portfolio of digital products.

This is an exciting opportunity to join a rapidly expanding machine learning engineering team at a pivotal stage of growth. The organization is investing heavily in recommendation systems, real-time personalization, machine learning platforms, and next-generation AI capabilities, offering engineers the opportunity to work with large-scale distributed systems and production-grade ML infrastructure.

RESPONSIBILITIES

• Design, build, and maintain scalable infrastructure supporting machine learning training, deployment, and inference workloads.
• Develop and optimize backend services, microservices, and cloud-native applications that power real-time machine learning systems.
• Own and enhance ML platform capabilities across cloud infrastructure, model serving, monitoring, and operational tooling.
• Partner closely with Data Scientists to productionize machine learning models and support real-time recommendation and personalization use cases.
• Improve CI/CD pipelines, infrastructure-as-code frameworks, observability, reliability, and system scalability.
• Participate in operational ownership, incident response, and support for critical production services.

SKILLS AND EXPERIENCE

Must-Have

• 7+ years of software engineering or machine learning engineering experience.
• Strong backend engineering expertise with experience building distributed systems at scale.
• Proven experience developing Scala-based microservices and production-grade backend applications.
• Deep knowledge of AWS cloud services, including machine learning infrastructure and managed platforms.
• Hands-on experience with Kubernetes, Docker, Terraform, and modern CI/CD practices.
• Strong Python programming skills.
• Track record of owning production systems, reliability, monitoring, and operational excellence.

Nice-to-Have

• Experience with machine learning infrastructure, MLOps, or model-serving platforms.
• Knowledge of recommendation systems, personalization engines, or CTR optimization.
• Experience with Datadog observability and monitoring.
• Background in adtech, fintech, e-commerce, or other high-scale consumer platforms.
• Exposure to real-time machine learning applications and online inference systems.

BENEFITS

• Competitive base salary and annual bonus
• Equity participation through RSUs
• Hybrid working model
• Opportunity to work on cutting-edge AI and machine learning initiatives
• Significant career growth and technical leadership opportunities
• Exposure to large-scale, real-time production systems

HOW TO APPLY

Please register your interest by submitting your CV via the Apply link on this page.

KEY TERMS

Lead Machine Learning Engineer | Machine Learning Engineering | Platform Engineer | ML Infrastructure | Scala | Python | AWS | SageMaker | Kubernetes | Docker | Terraform | CI/CD | Distributed Systems | Recommendation Systems | Real-Time Systems | MLOps | Backend Engineering | Cloud Infrastructure | Platform Engineering | Datadog | Fintech | Personalization | ML Platform | Software Engineering | Hybrid NYC | Technical Leadership

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Director, Quality Engineering

Overview

A large enterprise organisation is seeking a senior quality engineering leader to modernise software quality practices and drive the transition from traditional testing approaches to an automation-first quality engineering model.
This role is responsible for defining enterprise-wide quality strategy, establishing scalable testing and automation capabilities, advancing AI-enabled quality practices, and improving software delivery performance across multiple technology teams. The successful candidate will partner with engineering, product, architecture, security, data, operations, and business stakeholders to ensure technology solutions are delivered reliably, efficiently, and with appropriate controls.
The position plays a critical role in accelerating delivery velocity, improving product reliability, strengthening operational readiness, and fostering a culture of engineering ownership for quality.

Key Responsibilities

Quality Engineering Leadership & Transformation

• Develop and execute an enterprise quality engineering strategy that advances automation, engineering-led quality, and modern delivery practices.
• Create and manage a long-term roadmap covering automation tooling, testing frameworks, operating models, metrics, talent development, and process improvements.
• Establish organisation-wide quality standards across applications, platforms, digital products, and emerging technology solutions.
• Define appropriate testing approaches, including automated testing, manual validation, engineering-owned quality practices, and governance requirements.
• Drive improvements in release confidence, delivery speed, defect prevention, and overall quality outcomes.

Test Automation & Modern Quality Practices

• Lead the development and adoption of automation frameworks covering API, UI, integration, regression, accessibility, performance, and end-to-end testing.
• Implement AI-assisted testing capabilities such as automated test generation, intelligent test selection, defect analysis, test maintenance, and synthetic data support.
• Establish automation coverage goals, quality standards, and reporting frameworks.
• Embed automated testing into continuous integration and continuous delivery pipelines.
• Evaluate emerging tools and technologies that improve software quality and team productivity.

AI Product Quality & Evaluation

• Collaborate with technical and business stakeholders to define quality standards for AI-enabled products and intelligent workflows.
• Support the creation of evaluation frameworks that assess expected behaviours, acceptance criteria, guardrails, risk scenarios, and escalation paths.
• Ensure appropriate controls exist for accuracy, consistency, traceability, safety, monitoring, and operational readiness.
• Integrate testing, evaluation, monitoring, and feedback mechanisms into AI development lifecycles.
• Establish scalable approaches for validating AI-enabled solutions beyond manual review methods.

Release Quality & Operational Readiness

• Define release governance processes, quality gates, defect management standards, and production validation requirements.
• Establish quality metrics and release criteria for business-critical applications and technology services.
• Partner with delivery and operational teams to integrate quality into deployment, monitoring, rollback, and incident management processes.
• Oversee performance, accessibility, reliability, and non-functional testing practices.
• Drive continuous improvement in automation effectiveness, defect prevention, production stability, and release predictability.

Quality Organisation & Talent Development

• Lead and evolve the quality engineering operating model across a complex enterprise environment.
• Build, mentor, and develop quality engineering professionals with a focus on automation, engineering partnership, innovation, and continuous improvement.
• Clarify roles and responsibilities across manual testing, automated testing, quality engineering, product acceptance, and AI evaluation activities.
• Assess capability gaps, workforce planning requirements, upskilling opportunities, and partner support needs.
• Promote a culture of accountability, risk management, automation, and measurable customer outcomes.

Stakeholder Engagement & Governance

• Collaborate with cross-functional stakeholders to ensure quality requirements are identified early and incorporated throughout delivery.
• Establish governance forums, standards, metrics reviews, and operational playbooks.
• Ensure quality practices align with enterprise standards for security, privacy, compliance, accessibility, and risk management.
• Communicate quality strategy, programme progress, tooling decisions, risks, and performance metrics to senior leadership.
• Serve as a strategic advisor on major technology transformation and innovation initiatives.

Required Qualifications

Education

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline.
• Master’s degree preferred.

Experience

• 15+ years of experience in software engineering, software quality, quality engineering, test automation, or related technology functions.
• 7+ years of leadership experience managing QA, quality engineering, automation, or engineering teams.
• Demonstrated success transforming manual testing environments into automation-first quality engineering organisations.
• Experience leading multidisciplinary teams including quality engineers, automation engineers, SDETs, performance testers, analysts, and external partners.
• Proven experience embedding quality into Agile, DevOps, DevSecOps, CI/CD, release management, monitoring, and operational support practices.
• Strong track record partnering with engineering, product, architecture, security, operations, and business stakeholders.
• Experience supporting AI-enabled, data-intensive, customer-facing, or mission-critical systems is highly valued.

Technical Expertise

• Deep expertise in software quality engineering, automation strategy, and software delivery practices.
• Strong knowledge of API testing, integration testing, UI testing, performance testing, release validation, and test automation frameworks.
• Experience integrating automated testing into CI/CD pipelines and modern engineering workflows.
• Understanding of AI-assisted testing techniques and automation productivity tools.
• Familiarity with quality evaluation frameworks for AI-driven systems, including validation, monitoring, traceability, and risk management.
• Knowledge of security, privacy, compliance, accessibility, reliability, and governance considerations for enterprise technology environments.

Preferred Qualifications

• Experience leading enterprise-scale quality engineering transformation programmes.
• Experience establishing quality standards, automation strategies, testing roadmaps, quality metrics, and operational governance frameworks.
• Background supporting AI, machine learning, conversational AI, workflow automation, analytics, or data-driven applications.
• Experience working within regulated or highly governed industries.
• Experience managing vendor relationships, systems integrators, and distributed delivery teams.
• Demonstrated success leading through organisational change, emerging technologies, and evolving delivery models.

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