Senior Data Scientist

• Be the first dedicated data hire in a high-growth SaaS business
• Own the roadmap from dashboards to production ML
• Ship the company’s first live likelihood model within 6–12 months
• Hybrid working (Dublin, 2 days onsite)

If you’re a data scientist who wants real ownership, not just reporting and tickets, this is worth a look.

We’re working with a well-funded, high-growth SaaS company operating at the intersection of data, AI, and regulated digital environments. They’ve built a strong global customer base and are now entering the next stage of scale.

They have serious product ambition. They have data. They have an AI engineer.

What they don’t have yet is a dedicated senior data scientist to own it properly.

That’s the gap.

You’ll sit within Product, working directly with senior leadership.

Rough split:

• 30% product analytics / BI

There’s currently no live ML model in production, only early-stage work and offline models

Success in year one looks like getting the first production likelihood model live and working.

You won’t just optimise accuracy metrics. You’ll need to understand the business deeply enough to know when something is statistically right but commercially wrong.

• Deep exploratory analysis across a large, fast-growing dataset
• Feature engineering with the AI engineer
• Building and iterating on predictive models
• Designing and analysing experiments (A/B, bandits, optimisation frameworks)
• Monitoring drift, bias, and real-world performance

• Defining and standardising core product metrics
• Supporting CS and GTM with segmentation & performance analysis
• Reducing ad-hoc reporting through self-serve insight

This is hands-on. SQL. Python. Stats. Product conversations. The lot.

• 5+ years in product analytics/data science in a B2B SaaS environment
• Experience in a startup or scale-up
• Strong SQL (you’re comfortable with complex analytical queries)
• Python for analysis and modelling
• Solid grounding in experimentation and statistics
• Clear understanding of how ML models are trained, evaluated, and fail
• Comfortable working in ambiguity and moving quickly

You’ll do well here if you’re collaborative, commercially aware, and autonomous.

This isn’t a management role. It’s a senior IC role with real influence. xcfaprz

• Direct line into product strategy
• Equity in a scaling SaaS company
• A chance to build something foundational, not incremental

In 12–18 months, you’ll be able to point to a live ML system and say, “I built that.”

If this sounds like your kind of challenge, hit apply or drop me a message @