AI Data Scientist

Job Title: AI Scientist Location : Menlo Park, CA (Hybrid Onsite -4 Days Onsite Per Week) Duration : 6+ Months Contract or Fulltime Teams Meeting Interview Job Description: AI Engineer with 6–10 years of experience designing and deploying scalable AI/ML solutions for AdTech platforms covering targeting, bidding, personalization, attribution, and real-time analytics .The role requires strong engineering fundamentals with hands-on ML model development, data pipelines, and real-time decision systems, leveraging modern distributed and cloud-based architectures . Key Responsibiliti • esDevelop and deploy AI/ML models fo • r:Audience targeting & segmentati • onAd ranking & bidding optimizati • onAttribution & campaign performance modelli • ngFraud detection & anomaly detecti • onBuild and optimize end-to-end ML pipeline • s:Data ingestion, feature engineering, training, and inferen • ceBatch & real-time model servi • ngDesign real-time decisioning systems for high-throughput, low-latency environment • s.Collaborate with data engineers and architects to ensur • e:Scalable data pipelines (ETL/ELT, streamin • g)High-quality feature stores and model lifecycle manageme • ntDrive experimentation frameworks (A/B testing, causal inference) to continuously optimize performance metric • s.Ensure privacy-aware and compliant AI solutions aligned with data governance framework s. Required Qualificati • onsBachelor’s/Master’s in Computer Science, Data Science, AI/ML, or related fie • ld.6–10 years of experience in AI/ML engineering / Data Science engineering rol • es.Strong programming skills • in:Python (mandato • ry)Java or C++ (preferr • ed)Hands-on experience • in:ML frameworks (TensorFlow, PyTorch, XGBoo • st)Distributed processing (Spark, Fli • nk)Streaming systems (Kaf • ka)SQL & NoSQL databa • sesExperience building production-grade ML pipelines and scalable data syst ems Preferred Qualifica • tionsExperience in AdTech / MarTech / Retail Media ecosy • stemsExposur • e to:Recommendation sy • stemsReal-time bidding sy • stemsExperimentation platforms / A/B te • stingFamiliarity • with:Kubernetes, Docker, microser • vicesPrivacy and regulatory frameworks (GDPR, data compli ance)