{"id":3235113,"url":"https://alion.io/job/skydo-fraud-risk-manager","title":"Fraud Risk Manager","company":{"id":1325,"name":"Skydo","domain":"skydo.com","url":"https://alion.io/company/skydo","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Finance","role_family":"Finance","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SAS","optional":false}],"status":"live","first_seen_at":"2026-10-25T10:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-25T10:35:30Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"As the AML and Fraud Risk Manager, you will play a pivotal role in designing Skydo's AI-driven risk intelligence architecture across onboarding, transaction monitoring, fraud detection, chargebacks, and collections. This is a mid-senior-level role that combines risk strategy, data science, and compliance expertise. The ideal candidate will bring a deep understanding of AML/CFT risk, fraud typologies, model development, and data analytics in financial services or payments. You will work closely with the heads of Risk and Compliance, Engineering, and Product teams to design frameworks, predictive models, and automated decisioning engines that reduce financial crime exposure while enabling business growth.\n\nThe core responsibilities for the job include the following:\n\nMerchant Fraud Risk Strategy: \nDefine the end-to-end merchant fraud risk strategy across onboarding, payment authorization, settlements, and refunds.\nDevelop risk segmentation models and merchant-level risk bands (low/medium/high) using behavioral and transactional insights.\nEstablish frameworks for synthetic merchant detection, mule network identification, money laundering, and misuse of legitimate accounts.\nContinuously refine Skydo's risk appetite framework for merchant fraud exposure.\n\nData Science and Predictive Modeling: \nPartner with Data Science to build ML models for: Merchant onboarding risk scoring and early fraud detection.\nPredictive transaction risk models using network graph analysis and behavioral clustering.\nFraud typology prediction covering first-party, third-party, card-not-present (CNP), refund abuse, ATO (Account Takeover), and synthetic merchant fraud.\nChargeback and refund abuse prediction.\nDesign feature libraries (velocity checks, device fingerprinting, anomaly detection).\nLead model validation, back-testing, and production rollout with measurable impact metrics (TPR, FPR, recall).\n\nFraud Prevention Systems and Implementation: \nTranslate model outputs into real-time decisioning rules and integrate with Skydo's fraud detection engine.\nPartner with Product and Engineering to embed adaptive fraud controls in merchant onboarding and transaction flows.\nBuild mechanisms to identify, classify, and monitor fraud patterns across the lifecycle\nDefine feedback loops from case outcomes to continuously train and improve model performance.\nCollaborate with external data vendors and API partners to enhance risk visibility.\n\nAnalytics, Reporting, and Governance: \nBuild fraud performance dashboards, MIS, and early warning indicators.\nAnalyze fraud losses, false positives, and merchant lifecycle performance to refine strategy.\nPresent insights and recommendations to leadership and board-level risk committees.\nOwn the risk calibration framework, ensuring accurate alignment between model predictions and business outcomes.\n\nCross-functional Leadership and Collaboration: \nPartner with Compliance, Finance, and Product to align fraud and AML controls with regulatory and operational policies.\nProvide strategic inputs during product launches, bank partner discussions, and systems audits.\nMentor analysts and cross-functional teams on fraud analytics, typologies, and detection methodologies.\nPlay an active role in scaling analytical capabilities, vendor integrations, and automation projects.\n\nRequirements: \nProven ability to design, implement, and optimize analytical risk models.\nPrior experience with card or payments ecosystems (e. g., chargebacks, merchant fraud).\nExposure to risk modeling tools like R, Python, SAS and large-scale data pipelines.\nKnowledge of statistical modeling, machine learning, and predictive analytics techniques.\nDeep understanding of AML/CFT frameworks, FATF standards, and fraud typologies.\nStrong problem-solving, stakeholder management, and communication skills.\nQuantitative academic background (Engineering, Statistics, Economics, or Data Science) from a Tier-1 institute.\n3+ years of experience in fraud risk, AML, or data-driven risk management in fintech, banking, or payments.\nAnalytical thinker with high curiosity and data orientation.\nBalances business enablement with risk control rigor.\nThrives in fast-paced, startup environments with extreme ownership.\nSkills\nMachine Learning, Python, Predictive Modelling, Fraud Management, Risk Management, Risk 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now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":21,"reasons":["seen:2","velocity","win:early","comp:junior"],"computed_at":"2026-10-28T05:59:10Z"},"pay":null,"html_url":"https://alion.io/job/skydo-fraud-risk-manager","json_url":"https://alion.io/job/skydo-fraud-risk-manager.json","meta":{"generated_at":"2026-10-28T13:47:23Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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