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Titan OS
Titan OS
51 – 200 Employees
B2BHardwareMedia
Titan OS develops an independent European operating system for connected TVs, working with television manufacturers and content providers to make viewing more personalized and easier to navigate. Its content-first platform brings streaming services and linear channels together, helping viewers discover what to watch through tailored recommendations and access content with a single remote. The company also gives device makers and broadcasters tools for interface customization, app and channel integration, and audience monetization through revenue-sharing opportunities.
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Senior Data Scientist, CTV Audience Modeling – Titan OS (Barcelona Hybrid)

Develop predictive audience models for Titan OS’s Connected TV advertising platform. Use sampling theory and machine learning to extrapolate customer populations from imperfect data.

ESSpainHybridFull-timeSeniorEnglish RequiredData Scientist
CloudNumpyPandasPythonScikit-LearnSQL

Description

  • Expand seed and sample audiences into high-value cohorts across the broader Connected TV population
  • Use weighting, calibration, MRP, and hierarchical techniques to extrapolate reliably from imperfect or non-representative samples
  • Develop propensity, classification, similarity, ranking, and positive-unlabelled models to find users with strong affinity to seed audiences
  • Engineer reliable behavioural and contextual features from large-scale event data while limiting target leakage and model instability
  • Investigate selection bias, coverage gaps, and covariate shift, and explain uncertainty in audience estimates
  • Evaluate lift, precision and recall, calibration, and stability through holdouts, temporal and geographic testing, and observed campaign results
  • Deploy successful models through scalable pipelines with version control, drift detection, monitoring, and retraining
  • Work with Product, Commercial, and Engineering teams while establishing rigorous modelling standards across the Data team

Requirements

  • Bring at least 8 years of professional experience in Data Science, Applied Statistics, Quantitative Research, Econometrics, or a closely related discipline
  • Demonstrate deep statistical knowledge spanning sampling theory, estimation, inference, probability, uncertainty quantification, and experimental design
  • Have substantial experience generalising samples to populations using post-stratification, raking, calibration or propensity weighting, hierarchical models, or MRP
  • Have hands-on experience developing ML lookalike models from seed audiences, CRM or customer samples, or known converters using propensity, classification, similarity, ranking, or positive-unlabelled methods
  • Apply advanced machine learning and feature engineering across GLMs, XGBoost or LightGBM, ensembles, embeddings, and large behavioural datasets
  • Validate models using lift or gains, precision@K or recall@K, probability calibration, A/B testing, stability analysis, and drift monitoring
  • Work fluently with Python and SQL, including scikit-learn, pandas, and NumPy
  • Experience with large-scale cloud data platforms and production ML or MLOps is highly desirable
  • Communicate clearly and bring strong quantitative fundamentals
  • Hold a BS or MS in a relevant quantitative field; a PhD is advantageous but not required

Benefits

  • Competitive compensation
  • Private health insurance
  • A friendly, diverse, and international working environment
  • Professional development opportunities through challenging work in the fast-growing Connected TV industry
  • The chance to join a well-funded, high-growth company early and help shape a product and business expected to affect millions

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