[GBL] Product Data Scientist - 6-month project

Software Mind

17h•Remote

About The Position

Software Mind is seeking experienced Product Data Scientists to join their Data team during an exciting growth phase. The role involves being the analytical backbone of a pillar, acting as a strategic consultant to product, engineering, and commercial teams. This position requires blending rigorous statistical thinking with sharp commercial judgment, utilizing AI tools and automation for efficiency, and focusing human expertise on high-value problems. The goal is to drive the full analytical lifecycle from hypothesis to recommendation, shaping strategy, leading experimentation, and empowering teams with data tools and insights.

Requirements

Extensive experience in an analytical role, ideally within a product-led or digitally native environment.
Fluency in SQL.
Fluency in 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 and telling compelling, data-led stories to stakeholders at all levels.
Strong experiment design and statistical reasoning skills.
Comfort working across teams and managing multiple priorities without losing rigor.
Genuine curiosity about user behavior and a drive to understand the 'why' behind the data.
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 ensure insights lead to decisions.
Work with Product Managers, Engineers, and Senior Leaders to shape strategy.
Translate ambiguous business problems into clear analytical tasks.
Lead experimentation across your pillar, including designing tests, sizing opportunities, and interpreting results.
Build a culture of evidence-based iteration.
Create self-serve dashboards and data tools to empower teams.
Define and standardize metrics.
Use AI-assisted workflows for code generation and automated reporting.
Document work in the Insights Hub, champion consistent data practices, and share learnings.