Oxford Data Plan tracks revenue and other KPIs daily for 250+ listed companies globally using a mixture of first and third party data, and a custom modelling methodology . We work with around 100 investment funds with different strategic focuses, and recently closed a Series B investment round.

Data Research is one of the departments inside the Technology division. They are primarily responsible for finding new ways to build KPI trackers. Data Research works very closely with our Data Partnerships team, which is responsible for purchasing new data sets from third parties, and running first party data collection efforts (e.g. expert panels).

Data Research has broadly three routes to unlocking new KPI trackers:

1. Partnership data discovery. Data Partnerships searches for a prospective vendor with relevant data for a particular sector, and sends trial data to Data Research to evaluate the source’s usefulness for building new KPI trackers.

2. Research projects. Data Research generates hypotheses about how we could potentially track a new sector or set of KPIs, then provides this brief to Partnerships to find a potential data source.

3. Direct data discovery. Data Research searches for useful sources of information on the web, collect s this data, and builds prototype trackers to assess the value of the data.

The purpose of this role is to provide strong technical leadership to the department

and drive our data rese arch efforts across all three arms , along with promoting technical excellence across the team.

The Role

Reporting directly to the CTO, this role will lead the Data Research team, which currently consists of 6 individuals. Data Research is one of four primary departments within the Technology division. You will work closely with the leadership of the other four departments , and with the Data Partnership team, which sits outside of Technology.

Initially, the role will have at least one direct report namely the Data Evaluation Manager, who coordinates the day - to- day evaluation cycle of new data sources, and coordinates direct data discovery work. The team will be expected to grow quickly, and the number of direct reports is likely to grow in the future.

Key responsibilities of the role are:

Define ODP’s data research strategy: Work closely with the CTO, Head of Strategy and other key stakeholders to define the strategy of the data research team.
Deliver end-to-end data research projects; Define research hypotheses with the team to unlock new high-value KPI trackers; Kick-off and own the accountability of complex research projects to define and test hypotheses for unlocking new KPI trackers; Identify potential experts for consultation calls to develop sophisticated research angles that are under-explored in the industry.
Level up the data evaluation standard; Own accountability for the speed and quality of new data set evaluations; Define and enforce data evaluation standards across the team, with the goal of getting evaluations correct the first time around, and avoiding re-work; Continuously improve the evaluation process to expand our ability to assess novel data sources, including for product launches outside of our core KPI tracker offering.
Own and develop our data valuation discipline; work with the Data Evaluation Manager, Data Partnerships team, and CTO to improve cost-benefit analysis of new data sets to enable cost-effective data purchasing decisions.

Lead the Data Research team and drive technical excellence; Define the technical standard for the Data Research team to operate at, and enforce it; Hire top data research talent as the function grows; Drive a higher level of statistical and technical sophistication within the team; Build Data Research into a durable capability that does not depend on any single individual; Work with the Head of AI & DataOps and CTO to ensure that AI is being used to improve the overall quality and speed of our Data Research work.

Requirements

We are looking for a technically excellent data leader who can set direction as well as go deep on the data itself. The following are the requirements for the role.

We are a remote first company, but we prefer to hire candidates in the UK or Europe to be geographically close to the bulk of the team.

Education and experience: Around 10 years of relevant experience in data science, statistics or a closely related field. Postgraduate research counts towards this, so a PhD in a relevant area substitutes for roughly five years of that total and a master's degree for roughly two. This is a senior hire, and we expect at least three of those years to have been spent in a managerial or technical leadership role.

Technical depth in data and statistics: Strong quantitative background with hands-on experience in statistical analysis, data modelling and working with messy, real-world data sets. Proficient in Python and SQL, and able to review and challenge the technical work of the team.

Experience working with varied data sets: Demonstrable track record of work on feature engineering and modelling across a variety of domains.

Research leadership: Experience defining research hypotheses and owning complex, open-ended research projects end-to-end; from framing the question through to a clear, evidenced recommendation.

People leadership: Experience leading and growing a technical team, including hiring, setting standards and developing individual contributors. Comfortable managing managers as the function scales.

Commercial judgement: Ability to weigh cost against benefit when recommending data purchases, and to communicate the reasoning clearly to the CTO, Data Partnerships and other senior stakeholders.

Stakeholder communication: Able to explain technical findings and their limitations to non-technical audiences, and to work effectively across departmental boundaries.

Pragmatism and pace: Comfortable operating in a fast-moving, Series B environment where priorities shift and perfect information is rarely available.

Desirable

Experience in financial data, alternative data or investment research, and familiarity with how funds consume KPI data.

Exposure to first party data collection methods such as expert panels and surveys.

Experience applying AI to accelerate research and data quality workflows.

Experience building a research or data function from a small team into a scaled, durable capability.

Oxford DataPlan