The Role
We are hiring a Lead Data Scientist to establish and grow internal analytics capability.
This role will initially focus on partnering closely with Product and Engineering to provide high-quality analytical insight, support product decision-making, and improve our understanding of platform usage, performance, and outcomes.
Given the nature of data products, experience working with financial or market data is highly advantageous.
This is a hands-on leadership role. You will define analytical standards, shape the roadmap for analytics, and deliver high-impact analysis yourself. You will work across multiple systems and data sources including SaaS platforms and operational databases to discover, extract, and structure data needed to answer complex questions.
You will combine strong technical capability, people-leadership, product intuition, and business awareness while laying the foundations for a future analytics function.
Key Responsibilities
Establish the Analytics Function
- Define a vision, scope, and operating model for internal analytics in collaboration with the VP Data and Analytics.
- Identify high-impact analytical opportunities aligned with Product and Engineering priorities.
- Establish best practices for analysis, experimentation, metric definition, and insight communication.
- Build trust in analytics through rigor, clarity, and transparency of methodology.
Product & Engineering Partnership
- Partner closely with Product Managers and Engineering Leads to support roadmap decisions with data.
- Support the definition and refinement of product metrics, KPIs, and success criteria.
- Translate ambiguous product and system questions into structured analytical approaches.
Data Discovery & Extraction
- Discover, access, and extract data from SaaS platforms, APIs, and operational databases.
- Write queries and scripts to programmatically retrieve, combine, and prepare data for analysis.
- Work across structured and semi-structured datasets to build analysis-ready datasets.
- Identify gaps in instrumentation, tracking, or data availability and propose improvements.
- Collaborate with engineering teams to productionise repeatable data extraction or transformation where appropriate.
Insight Generation & Communication
- Conduct exploratory and deep-dive analysis to uncover trends, risks, and opportunities.
- Apply appropriate statistical and analytical methods to ensure robust conclusions.
- Present insights clearly to technical and non-technical stakeholders, focusing on actionability.
- Build dashboards or lightweight reporting where appropriate, while maintaining a focus on meaningful metrics.
Grow & Scale the Capability
- Expand analytics support beyond Product & Engineering over time.
- Coach and mentor team members, monitoring performance and ensuring alignment with business needs.
- Help define future hiring needs and capability gaps.
- Contribute to building a scalable, organisation-wide analytics function.
Essential Criteria
- Significant experience in analytics, data science, or product analytics within a SaaS or technology environment.
- Experience in team leadership within a growing organization.
- Strong SQL skills and experience working with large-scale datasets.
- Demonstrated experience discovering, accessing, and programmatically extracting data from SaaS platforms, APIs, and operational databases.
- Experience writing scripts (e.g. Python or similar) to retrieve, combine, and transform data for analysis.
- Strong statistical reasoning and ability to select appropriate analytical methods.
- Experience partnering closely with Product and Engineering teams.
- Exceptional communication skills, with an ability to translate ambiguous business questions into structured, data-driven analysis.
- Comfortable operating independently and setting direction in an emerging function.
Desirable Criteria
- Experience in financial services, particularly working with trading, securities, market, or regulatory data (highly desirable).
- Experience establishing or scaling an internal analytics function.
- Familiarity with modern analytics tooling (e.g. dbt, BI tools, experimentation platforms).
- Experience shaping event tracking or instrumentation standards.
- Exposure to data governance and metric definition frameworks.
