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Staff Data Scientist, Product

Location: Open / Hybrid / Remote (role-dependent)

Level: Senior Individual Contributor

Role Overview

This role sits within a growing Product Analytics & Data Science team and serves as the technical anchor for experimentation, causal inference, and advanced statistical methods. The Staff Data Scientist will play a critical role in elevating how experiments are designed, executed, interpreted, and trusted across the organization.

Beyond technical execution, this individual will be responsible for shaping experimentation culture, improving analytical rigor, and influencing product and executive stakeholders without direct authority. This is a high-impact IC role for someone who thrives at the intersection of data science depth, product thinking, and organizational influence.

Key Responsibilities

Experimentation & Causal Inference

• Own and evolve the experimentation framework across web and in‑product surfaces
• Design, review, and debug experiments to ensure statistical validity and consistent user experiences
• Apply Bayesian methods, sequential testing, and multi‑arm bandits to real product decisions
• Establish best practices for experimentation design, analysis, and rollout
• Act as the go‑to expert for causal inference questions across the product organization

Product Analytics & Modeling

• Partner with Product, Engineering, and Design to answer complex questions around:
• Feature adoption and usage
• User success and expansion
• Retention and churn drivers
• Build and deploy analytical models that inform product strategy and prioritization
• Translate ambiguous product questions into rigorous analytical approaches with clear recommendations

Technical Leadership & Coaching

• Review analytical approaches and model implementations from other team members
• Coach and mentor data scientists and analysts on advanced statistical techniques
• Set the technical bar for experimentation and analytical quality within the team
• Promote reproducible, well‑documented, and reviewable analytics work

Stakeholder Influence

• Educate non‑technical stakeholders on experimentation, uncertainty, and decision‑making
• Advocate for statistically sound practices in environments where experimentation maturity is low or fragmented
• Build trust in data science outputs through clear storytelling and business‑aligned insights
• Partner with leadership to define and prioritize the experimentation roadmap

Tooling & Infrastructure

• Contribute to the evaluation and implementation of experimentation platforms and tooling
• Work in a collaborative, code‑first environment using version control and peer review
• Ensure analytics and experimentation workflows are scalable and reliable

Required Qualifications

• 6+ years of experience in data science, product analytics, or related fields
• Hands-on expertise in experimentation methodologies, including:
• Bayesian analysis
• Causal inference
• A/B and multivariate testing
• Sequential testing or multi‑arm bandits
• Strong product analytics background within a SaaS or digital product environment
• Proficient in Python and SQL
• Comfortable working in Git-based workflows (PRs, code reviews)
• Experience deploying or productionizing at least one analytical or ML model
• Proven ability to connect technical work to business outcomes

Preferred Qualifications

• Experience evaluating or implementing experimentation tooling
• Exposure to user lifecycle analytics (activation, adoption, retention, churn)
• Familiarity with modern analytics engineering or experiment platforms
• Comfort working in ambiguous environments with evolving data maturity

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Head of Data Strategy & Governance
Central London – 2 days a week in office
Salary: £130,000 – £160,000

This is a senior data leadership role with genuine influence. You will take ownership of how data is governed, trusted and scaled across a fast-growing, technology-led financial services business. With data at the heart of decision-making, this role offers the opportunity to shape strategy, solve complex quality challenges, and build a robust operating model that supports long-term growth.

The Company
They are a technology-driven financial services organisation operating at the intersection of finance and data. Their platform brings together complex financial information to provide clients with a clear, holistic view of their finances and support better long-term decisions. The business has grown rapidly and continues to invest heavily in its data and technology capabilities. The culture is collaborative, delivery-focused, and values ownership and clarity.

The Role
As Head of Data Strategy and Governance, you will define and embed how data is owned, managed, and trusted across the organisation. You will work closely with technology, product, and senior stakeholders to ensure data quality underpins every insight and client outcome.

Key responsibilities include:

• Defining and delivering the data strategy, operating model, and governance framework across the business
• Establishing clear data ownership, standards, and accountability in a federated environment
• Leading data quality initiatives to address real-world issues across multiple data sources
• Building and maintaining a single source of truth, including data dictionaries and master data approaches
• Overseeing the data platform within an AWS and Databricks environment, ensuring scalability and reliability
• Providing strategic oversight of BI and analytics, partnering with teams to enable trusted reporting
• Managing and developing a multi-disciplinary data team spanning operations, governance, and BI

Your Skills and Experience

• Strong commercial experience in data strategy, data governance, and operating model design
• Deep understanding of data quality management, controls, and frameworks
• Experience working with modern cloud data platforms, including AWS and Databricks
• Ability to translate complex data and governance concepts for technical and non-technical stakeholders
• A pragmatic, delivery-focused mindset with the confidence to lead change in a scaling organisation
• Background in regulated or data-intensive environments is beneficial

What They Offer

• Competitive salary with flexibility depending on experience
• Hybrid working with two days a week in a central London office
• Private medical insurance with a comprehensive wellbeing and fitness programme
• 25 days annual leave with the option to carry over additional days
• Pension via salary sacrifice, plus access to multiple salary sacrifice schemes
• Enhanced parental leave, mental health support, and a dog-friendly office
• Study support, time for learning, and home working equipment on request
• Clear scope for progression and influence, with potential equity reviewed after probation

How to Apply
If you are looking to lead data strategy and governance in a growing, data-led organisation, apply now to find out more.

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Senior AI Engineer (Agentic Systems & LLM Applications)
San Francisco, CA (Hybrid) Tuesday – Thursday (Onsite)
$180,000 – $220,000 base + equity + benefits

About the Company

This organization builds software that helps businesses operate with greater transparency, accountability, and trust. Its platform supports critical workflows around governance, risk, and operational decision-making, enabling teams to manage complex processes with speed and confidence.

The company has reached meaningful scale and is now investing heavily in its next phase – embedding advanced AI directly into core product workflows. This is not an experimentation layer; these are production systems that customers rely on to make real decisions.

Engineering teams operate with high ownership and low overhead. The environment rewards people who take initiative, challenge assumptions, and deliver systems that work in the real world.

About the Role

This is a high-ownership, builder role for engineers who want to push beyond “feature-level AI” and design systems that actually reason, act, and operate in production.

You will own end-to-end development of AI systems – from architecture and design through deployment, evaluation, and iteration. The focus is on building agentic systems and retrieval-driven pipelines that can handle complex, multi-step workflows across large volumes of structured and unstructured data.

This role is best suited for engineers who have already shipped LLM-based systems and want to go deeper – improving system reliability, designing evaluation frameworks, and building AI that can be trusted in high-stakes environments.

You’ll work closely with product and engineering leadership to define what gets built, not just how it gets built.

Key Responsibilities

• Architect and deploy production LLM systems capable of multi-step reasoning and decision support
• Build and scale agentic workflows that automate complex, long-running processes
• Design end-to-end RAG pipelines (ingestion, embeddings, retrieval, ranking, generation)
• Develop evaluation frameworks and guardrails to ensure system accuracy, reliability, and safety
• Optimize systems for latency, cost efficiency, and production performance
• Partner with product and engineering leaders to identify and prioritize high-impact AI opportunities
• Contribute to backend systems and infrastructure supporting large-scale AI deployment

Must Haves

• 7+ years of software engineering experience, including 2+ years building AI/ML systems
• Strong Python expertise and experience building production-grade systems
• Proven track record shipping LLM-based applications into production
• Hands-on experience with RAG pipelines, embeddings, and vector databases
• Experience building agent-based or multi-step AI systems
• Strong system design skills with experience in distributed systems
• Clear ownership of systems from initial concept through production and iteration

Nice to Have

• Experience with workflow orchestration or distributed task systems
• Familiarity with backend systems or TypeScript-based services
• Experience working with document-heavy or high-complexity datasets
• Exposure to evaluation frameworks, monitoring systems, or AI safety practices

Why Join

• Build AI systems that operate in real-world, high-stakes environments – not prototypes
• Own the architecture and evolution of agentic systems used by real customers
• Work on problems where correctness, reliability, and trust actually matter
• High autonomy with direct influence on product direction and technical strategy
• Strong compensation, meaningful equity, and comprehensive benefits
• In-person collaboration Tuesday-Thursday with a focused, high-caliber engineering team
• Opportunity to raise the technical bar alongside engineers who have shipped real systems

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