Senior Data Scientist

Titan OS

. Build models that expand seed and sample audiences into high-value cohorts across the wider CTV population .

Posted 10/9/2026full-timeBarcelona • SpainSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced expertise in Data Science and Applied Statistics, with a focus on model building, feature engineering, and statistical validation. Proficient in leveraging large-scale datasets and cloud platforms to create scalable machine learning solutions.

Highest-signal resume keywords

Data Science ExpertiseStatistical ModellingMachine Learning TechniquesPython and SQL ProficiencyModel Validation Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ExpertiseSampling TheoryEstimationMachine LearningFeature EngineeringModel ValidationPost-StratificationHierarchical ModelsPropensity WeightingExperimental Design

Soft Skills

Strong CommunicationQuantitative Foundations

Tools & Technologies

PythonSQLScikit-learnPandasNumPyXGBoostLightGBMCloud Data PlatformsMLOps

Industry Keywords

Data ScienceApplied StatisticsQuantitative ResearchEconometricsMachine Learning Operations

Tech Stack

Tools & technologies

CloudNumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

8+ years of professional experience in Data Science, Applied Statistics, Quantitative Research, Econometrics or a closely related field
Deep statistical expertise in sampling theory, estimation, inference, probability, uncertainty quantification and experimental design
Strong sample-to-population experience using post-stratification, raking, calibration/propensity weighting, hierarchical models, or MRP
Hands-on ML lookalike experience using seed audiences, CRM/customer samples or known converters with propensity, classification, similarity/ranking or positive-unlabelled methods
Advanced ML and feature engineering skills across GLMs, XGBoost/LightGBM, ensembles, embeddings and large-scale behavioural datasets
Strong model validation skills including lift/gains, precision@K/recall@K, probability calibration, A/B testing, stability and drift monitoring
Advanced Python and SQL skills with scikit-learn, pandas/NumPy
Experience with large-scale cloud data platforms and production ML/MLOps are highly desirable
Strong communication and quantitative foundations
BS/MS in a relevant quantitative field; PhD is advantageous but not required

Benefits

Comp & perks

Competitive compensation
Private health insurance
Friendly, diverse, and international work environment
Opportunity to work outside of your comfort zone and develop professionally in an exciting and fast-growing CTV industry
Opportunity to join a well-funded, high-growth company in the early stages and help shape a product/business that will impact millions