Senior Data Analyst

Job Description

At a glance

Prolific: A fast-growing remote-first scale-up of 150+ people who are mainly based in the UK. YC alumni (summer 2019) and Series A funding of $32m in 2023.

Our mission is to revolutionize how the world learns about people, so people can revolutionize the world.

Salary: £65,000-70,000 + bonus + share options

The Role

As a Senior Data Analyst at Prolific, you’ll be a key connective player between several different business units: working closely with product managers, finance-team members, data-scientists and user researchers (and other analysts of course) to inform product development, optimize performance, and measure the impact of new initiatives using data.

Although your home will be in ‘central data’, your focus will be on supporting two product teams. The first of these is the Data Collection & Partnerships team, a cross functional team responsible for developing novel data collection tools for our customers. Their domain is the AI-teaching market, where efficient task allocation and randomisation is crucial. We already have significant traction in this market, but there is considerably more work to be done! The second product team you’ll support is the payments and pricing team, whose work underpins our participant reward system and our researcher pricing.

As a senior data analyst, you’ll design AB tests, identify areas for product improvement and help us understand customer behavior. There’ll be opportunities to serve in an analytics engineering capacity, helping refine our data-models for ease of use and simplicity, and of course, you'll also be an integral part of a smart and motivated data team, working at the cutting edge of Prolific technology.

What you'll be doing

• You’ll be responsible for painting a high definition picture of how our researcher’s collect data. You’ll design click-tracking, data models and dashboards to help us understand this new vertical. Working closely with the DCP team to implement and assess the success of new product features.
• You’ll also support the Payment and Pricing team as they make our payments platform ever more robust and flexible. This will involve writing data contracts and checking the integrity of data flows as the team pioneers a major re-architecting of our app’s backend. In addition to teaming up with our data science function to develop and test new pricing models.
• Of course you’ll also react to the day-to-day data needs of both teams with dashboards, reports and ad-hoc queries. You’ll mostly be leveraging the upper half of our data tech stack for your work. This means working mostly with cleaned and modeled data, where a lot of the business logic and testing has already been taken care of. That said, you’ll sometimes need to design new data models in DBT in order to surface newly collected behavioral data.
• When time allows, you’ll also be a source of flexible analytical capacity, with the opportunity to get involved across the whole business, and on a wide range of projects: anything from analytics & data engineering through to machine-learning and teaching.

What you’ll bring

• Considerable experience writing analytic SQL, including window-functions, CTEs, and JOINs. You have dabbled (at least) with dbt, and have a working knowledge of python.
• An eye for detail: you're able to get into the weeds of data-problems, understanding the source of unusual patterns in the data, and verifying that top level conclusions hold under close scrutiny.
• Great analytical skills, with a track record of digesting complex data, identifying trends, and drawing meaningful insights.
• A passion for using data to guide product design and a hunger for solving problems, even if by unconventional means.
• Competency in common stats for product analytics and AB tests: i.e. you can run a sample size calculation, perform unbiased allocation to groups, choose the right test for the design, identify potential confounding factors.
• Strong visual communication skills, with the ability to effectively share data-driven insights and recommendations to non-technical stakeholders.

Even better if you have

• Knowledge of modern generative AI models and how they’re taught.
• Experience analyzing the behavioral data generated by customer’s API use.
• Experience with online recruitment and survey platforms as well as a knowledge of the current online research landscape.