{"id":2493379,"url":"https://alion.io/job/appsflyer-senior-data-scientist","title":"Senior Data Scientist","company":{"id":3446,"name":"AppsFlyer","domain":"appsflyer.com","url":"https://alion.io/company/appsflyer","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":10,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":53,"computed_at":"2026-10-14T06:34:16Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Herzliya, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":86000,"max_usd":221000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2157},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AppsFlyer SDK","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"BigQuery","optional":true},{"name":"Google BigQuery","optional":true},{"name":"PyTorch","optional":true},{"name":"Scala","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-13T09:20:03Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T12:54:39Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"AppsFlyer is a cutting-edge technology company that specializes in mobile attribution and marketing analytics.\nWe run at a massive scale- t any given moment thousands of servers are consuming 150+ billion mobile app events, crunching our users’ data, serving requests and communicating on a massive scale.\nFueled by the AppsFlyer platform, Protect360 is the leading anti-fraud solution in the market, protecting advertisers from fraudulent traffic and saving millions of dollars for our customers.\nWe detect fraudulent traffic (fake installs and attribution hijacking attempts) in real time using machine learning, deep learning, and advanced AI techniques applied on massive data streams.\nWe’re looking for a data scientist to join our team and further improve fraud detection capabilities.\nThis position involves analyzing evolving fraud patterns, developing and deploying advanced prevention components, and leveraging domain expertise, data tools, and modern AI methods to stay ahead of emerging threats.\nA partial list of our tech stack: Python (and its related scientific stack), Spark, TensorFlow/PyTorch, Kafka, AWS services, Airflow, Scala, BigQuery, and more\nWhat you'll do\nMine massive data sets to uncover fraud patterns and prevention opportunities.\nDevelop and productionize large-scale ML, DL, and AI models to detect and prevent fraudulent activity.\nTranslate business and product needs into scientific, explainable, and high-impact solutions.\nDesign and maintain end-to-end ML pipelines - from data ingestion and feature engineering to serving and monitoring in production.\nCollaborate closely with product, engineering, and business teams to drive data-driven decisions.\nStay current with AI and ML research and evaluate new technologies to strengthen Protect360’s algorithms.\nWhat you have\nM.Sc in Computer Science, Mathematics, Statistics, Engineering or a related field\n5+ years of experience in applied Data Science or Algorithmic Development, preferably in large-scale production environments.\nHands-on experience in training and fine-tuning machine learning, deep learning, and AI models\nStrong programming skills in Python and proficiency in SQL.\nSolid understanding of data pipelines, MLOps practices, and working with huge datasets.\nAbility to mentor, collaborate, and communicate complex ideas clearly to diverse audiences.\nA fast learner, problem solver, and end-to-end owner with a strong “can-do” approach.\nBonus Points\nExperience in the ad-tech industry or with fraud detection systems.\nFamiliarity with LLMs, generative AI, or representation learning for anomaly detection.\nProven track record working with large-scale distributed data systems.\nBeing introduced by an AppsFlyer employee\nAs a global company operating from 20 offices worldwide, we reflect the human mosaic of the diverse and multicultural world in which we live. We ensure equal opportunities for all of our employees and promote the recruitment of diverse talents to our global teams without consideration of race, gender, culture, or sexual orientation. We value and encourage curiosity, diversity, and innovation from all our employees, customers, and partners.\n“As a Customer Obsessed company, we must first be Employee Obsessed. 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