Senior Data Scientist

Is this you?

Titan Operating System S.L. (Titan OS), the Barcelona-based technology, entertainment, and advertising company, is looking for you!

At TitanOS, we live by three core values:

• Make things happen – We take ownership, move fast, and deliver impact.
• No ego – We collaborate with respect and humility to reach shared goals.
• Show genuine passion – We love what we do and never stop learning.

Culture and environment are at the heart of our ethos. If the above resonates with you, keep reading because we believe you could be a perfect addition to our incredible team!

Role overview:

We are looking for a Senior Data Scientist to lead the development of predictive audience models for Titan OS, turning smaller seed and observed datasets into robust predictions of much larger customer populations for Connected TV advertising. This is a deeply technical role focused on statistics, sampling theory and machine learning, with a strong emphasis on representativeness, selection bias, population extrapolation and uncertainty.

You will combine Titan OS first-party data with seed audiences and population-level datasets, owning the methodology from research and experimentation through validation, productionisation and model monitoring. Working closely with Data Engineering, Product, Commercial and Advertising teams, you’ll be a senior technical voice responsible for challenging assumptions, communicating uncertainty and setting rigorous modelling standards with direct commercial impact.

Key responsibilities:

• Build models that expand seed and sample audiences into high-value cohorts across the wider CTV population.
• Apply weighting, calibration, MRP and hierarchical methods to extrapolate reliably from imperfect or non-representative samples.
• Build propensity, classification, similarity, ranking and positive-unlabelled models to identify users with high affinity to a seed audience.
• Create robust behavioural and contextual features from large-scale event data while preventing target leakage and instability.
• Diagnose selection bias, coverage gaps and covariate shift, and communicate uncertainty in audience estimates.
• Measure lift, precision/recall, calibration and stability using holdouts, temporal/geographic tests and observed campaign outcomes.
• Move successful models into scalable pipelines with versioning, drift detection, monitoring and retraining.
• Partner with Product, Commercial and Engineering teams while setting rigorous modelling standards across the Data team.