A lot of data science roles talk about AI and machine learning in broad terms, but don’t always give you the chance to see your work make a real impact.

In this role, you’ll be applying your skills to build and deploy models that genuinely shape how the product works and how decisions get made — not just experiments that sit in notebooks.

What you’ll be doing:

• Designing, training, and deploying models that actually make a difference (yes, that includes propensity models, not just “toy” classifiers).
• Working with big data systems like Databricks, Spark, Snowflake — the sort of platforms where scale isn’t just a word on a slide deck.
• Productionising models in the cloud (AWS, Azure, GCP — pick your flavour).
• Building pipelines that keep data flowing without becoming someone else’s nightmare.
• Using proper statistical techniques to answer actual business questions, not just academic ones.
• Partnering with engineering teams so the models you build don’t rot on someone’s laptop.

What you’ll need to bring:

• A background in Computer Science, Stats, or something equally technical.
• Strong Python and SQL skills (the stuff you can’t Google in the interview).
• Experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn.
• Spark, Hadoop, Databricks, Snowflake, Redshift, take your pick.
• The ability to explain complex models without putting non-technical people to sleep.

Extra credit if you’ve:

• Used ML Ops tools like MLflow, SageMaker, or Vertex AI.
• Played around with transformer architectures or deep learning frameworks and actually deployed them.
• Built models that predict customer behaviour, churn, or conversion, and saw them drive commercial impact.

What’s in it for you:

• The chance to define data strategy instead of inheriting one written in 2015.
• A culture where growth and learning aren’t just buzzwords.
• Work that directly shapes the product, not hidden away in a PowerPoint deck.

If you’re a data scientist who wants to do more than build nice graphs in Jupyter notebooks, this is worth your time.

[email protected]