[GBL] Product Data Scientist - 6-month project

Software Mind

12h•Remote

About The Position

Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities – these are a few words that describe an average day for us. Building cross-functional engineering teams that take ownership and crave more means we’re always on the lookout for talented people who bring passion and creativity to every project. Our culture embraces openness, acts with respect, shows grit & guts and combines employment with enjoyment. We’re looking for experienced Product Data Scientists to join our Data team in an exciting growth phase to help shape how we do things as we continue to evolve. You'll be embedded as the analytical backbone of your pillar, acting as a strategic consultant to product, engineering, and commercial teams. That means blending rigorous statistical thinking with sharp commercial judgement, knowing when to run a full experiment and when a well-reasoned analysis is enough to move fast. You'll use AI tools and automation intelligently to work at pace, scale your output, and focus your energy where human judgement matters most.

Requirements

Extensive experience in an analytical role, ideally within a product-led or digitally native environment.
Fluency in SQL and at least one scripting language (Python or R).
Experience with dbt is essential.
Hands-on experience with BI tools such as Lightdash, Looker, or Tableau.
A track record of influencing decisions, you know how to tell a compelling, data-led story to stakeholders at all levels.
Strong experiment design and statistical reasoning skills, you understand when the numbers are telling you something real.
Comfort working across teams and managing multiple priorities without losing rigour.
Genuine curiosity about user behaviour and a drive to understand the why behind the data.
An openness to integrating AI tools into your workflow and a mindset for continuous improvement.

Responsibilities

Drive the full analytical lifecycle in your pillar, from hypothesis to recommendation.
Define the right metrics, build the right models and make sure your insights land, not just in a deck, but in decisions.
Work shoulder-to-shoulder with Product Managers, Engineers, and Senior Leaders to shape strategy.
Translate ambiguous business problems into clear analytical tasks and bring a point of view, not just a chart.
Lead experimentation across your pillar, designing tests, sizing opportunities, interpreting results, and building a culture of evidence-based iteration.
Create self-serve dashboards and data tools that empower your teams to answer their own questions.
Define and standardise metrics so everyone's working from the same source of truth.
Use AI-assisted workflows, from code generation to automated reporting, to accelerate your output and spend more time on the high-value analytical problems that require your expertise.
Document your work in our Insights Hub, champion consistent data practices, and actively share learnings across the data team.