‹ Back
Head of Data
JOB SUMMARY
Job details
About the Role
As our
Head of Data, you will build and scale Scrambly’s data science function from the ground up.
You’ll define the long-term strategy and lead hands-on execution across machine learning, personalization, experimentation, forecasting, fraud prevention, and internal decision intelligence.
This is a high-impact leadership role for someone who thrives at the intersection of product, engineering, and analytics.
You will develop models that directly influence user engagement, monetization, and risk management across global consumer products.
You will initially manage an external data science/ML agency while building Scrambly’s internal data science and AI team.
You will also own Scrambly’s applied AI strategy, including LLM-powered features and internal AI tools. Key ResponsibilitiesStrategy LeadershipDefine and own Scrambly’s data science roadmapBuild and lead the data science team: hiring, mentoring, establishing standardsManage and coordinate an external ML agency until the internal team is establishedCollaborate closely with engineering, product, design, marketing, operations, fraud, and leadership teams to drive data-informed decisionsTranslate complex technical concepts into clear recommendations for business stakeholdersLead Scrambly’s applied AI/LLM strategy across product and internal toolingMachine Learning PersonalizationBuild and maintain models for personalization, recommendations, engagement prediction, churn, and LTV forecastingDeploy and evaluate ML models across online/offline pipelines with continuous retraining and monitoringPartner with engineering to integrate ML models into Scrambly’s backend and mobile/web products with performance and scalability in mindDesign and integrate LLM-based capabilities such as semantic search, natural language assistance, automated content generation, or model-augmented decision enginesEvaluate, fine-tune, or distill foundation models (OpenAI, Claude, Llama, Gemini, etc. ) for Scrambly’s product and internal workflowsFraud Detection Risk PreventionDevelop advanced fraud detection systems using anomaly detection and behavioral modelingBuild scoring systems for event manipulation, multi-accounting, and suspicious behaviorWork with product and operations to design block rules, thresholds, TTC validation systems, and automated risk classificationMonitor fraud trends and proactively identify emerging risksData Infrastructure Model OpsDefine
requirements
and collaborate with engineering to build scalable data pipelines for training, inference, experimentation, and monitoringDefine and implement robust MLOps practices (CI/CD for ML models, automated retraining, monitoring, versioning)Ensure reliable data quality, documentation, and metadata standards across the organizationBuild infra for LLM workloads: vector DBs, embedding pipelines, prompt evaluation, safety checksCompliance, Privacy Responsible AIEnsure data science initiatives comply with global privacy regulations (GDPR, Japan APPI, Korea PIPA, CCPA)Work with the DPO/legal team to design privacy-by-design data science solutionsEstablish guardrails for responsible, safe, and transparent AI/LLM usage
Requirements
Technical Expertise6+ years of experience in data science or ML engineering roles, at least 2+ years in a leadership or senior ownership positionStrong proficiency in Python, ML frameworks (TensorFlow, PyTorch, XGBoost, etc. ), and analytics toolingDeep experience building and deploying ML models in production environmentsHands-on expertise in recommendation systems, personalization, time-series forecasting, and/or fraud detection modelsExperience with data pipeline tools (Airflow, dbt, BigQuery, Snowflake, etc. ) and MLOps best practicesExcellent communication skills in English (B2+); ability to collaborate with multidisciplinary teams across countriesBusiness Leadership SkillsProven ability to translate business goals into data science solutions that drive measurable impactStrong analytical thinking and ability to design rigorous experimentsExperience working closely with product, engineering, monetization and marketing teamsExcellent communication skills and the ability to influence cross-functional decision-making
Product
-oriented mindset — able to balance accuracy, speed, and business contextExperience managing teams and/or coordinating work with external vendors or agenciesExperience working in fast-paced consumer/mobile product environments preferredWhat We OfferAn opportunity to define, build and own Scrambly’s data science AI function - with the mandate to shape vision, ship solutions, and drive measurable product impact in a fast-scaling businessDirect collaboration with the founders, engineering leadership, and product teamsMassive influence - your models and systems will shape the experiences of millions of users worldwideCompetitive compensation and a flexible, high-autonomy work environmentA chance to innovate in a fast-growing, data-rich business with complex and unique modeling challenges.
As our
Head of Data, you will build and scale Scrambly’s data science function from the ground up.
You’ll define the long-term strategy and lead hands-on execution across machine learning, personalization, experimentation, forecasting, fraud prevention, and internal decision intelligence.
This is a high-impact leadership role for someone who thrives at the intersection of product, engineering, and analytics.
You will develop models that directly influence user engagement, monetization, and risk management across global consumer products.
You will initially manage an external data science/ML agency while building Scrambly’s internal data science and AI team.
You will also own Scrambly’s applied AI strategy, including LLM-powered features and internal AI tools. Key ResponsibilitiesStrategy LeadershipDefine and own Scrambly’s data science roadmapBuild and lead the data science team: hiring, mentoring, establishing standardsManage and coordinate an external ML agency until the internal team is establishedCollaborate closely with engineering, product, design, marketing, operations, fraud, and leadership teams to drive data-informed decisionsTranslate complex technical concepts into clear recommendations for business stakeholdersLead Scrambly’s applied AI/LLM strategy across product and internal toolingMachine Learning PersonalizationBuild and maintain models for personalization, recommendations, engagement prediction, churn, and LTV forecastingDeploy and evaluate ML models across online/offline pipelines with continuous retraining and monitoringPartner with engineering to integrate ML models into Scrambly’s backend and mobile/web products with performance and scalability in mindDesign and integrate LLM-based capabilities such as semantic search, natural language assistance, automated content generation, or model-augmented decision enginesEvaluate, fine-tune, or distill foundation models (OpenAI, Claude, Llama, Gemini, etc. ) for Scrambly’s product and internal workflowsFraud Detection Risk PreventionDevelop advanced fraud detection systems using anomaly detection and behavioral modelingBuild scoring systems for event manipulation, multi-accounting, and suspicious behaviorWork with product and operations to design block rules, thresholds, TTC validation systems, and automated risk classificationMonitor fraud trends and proactively identify emerging risksData Infrastructure Model OpsDefine
requirements
and collaborate with engineering to build scalable data pipelines for training, inference, experimentation, and monitoringDefine and implement robust MLOps practices (CI/CD for ML models, automated retraining, monitoring, versioning)Ensure reliable data quality, documentation, and metadata standards across the organizationBuild infra for LLM workloads: vector DBs, embedding pipelines, prompt evaluation, safety checksCompliance, Privacy Responsible AIEnsure data science initiatives comply with global privacy regulations (GDPR, Japan APPI, Korea PIPA, CCPA)Work with the DPO/legal team to design privacy-by-design data science solutionsEstablish guardrails for responsible, safe, and transparent AI/LLM usage
Requirements
Technical Expertise6+ years of experience in data science or ML engineering roles, at least 2+ years in a leadership or senior ownership positionStrong proficiency in Python, ML frameworks (TensorFlow, PyTorch, XGBoost, etc. ), and analytics toolingDeep experience building and deploying ML models in production environmentsHands-on expertise in recommendation systems, personalization, time-series forecasting, and/or fraud detection modelsExperience with data pipeline tools (Airflow, dbt, BigQuery, Snowflake, etc. ) and MLOps best practicesExcellent communication skills in English (B2+); ability to collaborate with multidisciplinary teams across countriesBusiness Leadership SkillsProven ability to translate business goals into data science solutions that drive measurable impactStrong analytical thinking and ability to design rigorous experimentsExperience working closely with product, engineering, monetization and marketing teamsExcellent communication skills and the ability to influence cross-functional decision-making
Product
-oriented mindset — able to balance accuracy, speed, and business contextExperience managing teams and/or coordinating work with external vendors or agenciesExperience working in fast-paced consumer/mobile product environments preferredWhat We OfferAn opportunity to define, build and own Scrambly’s data science AI function - with the mandate to shape vision, ship solutions, and drive measurable product impact in a fast-scaling businessDirect collaboration with the founders, engineering leadership, and product teamsMassive influence - your models and systems will shape the experiences of millions of users worldwideCompetitive compensation and a flexible, high-autonomy work environmentA chance to innovate in a fast-growing, data-rich business with complex and unique modeling challenges.
Discover the company
Explore other offers from this company or learn more about AllCares.
The company
A
AllCares Germany, Portugal, Romania, Spain
