{"id":1555886,"url":"https://alion.io/job/american-express-analyst-data-science-6","title":"Analyst-Data Science","company":{"id":2209,"name":"American Express","domain":"americanexpress.com","url":"https://alion.io/company/american-express","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":38,"ghost_share":0.026,"stale_share":0.079,"repost_share":0.079,"time_to_fill_p50_days":6,"computed_at":"2026-10-01T05:45:00Z"}},"role":"Analytics","role_family":"Analytics","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":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":10000,"max_usd":25000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":848},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-30T16:51:41Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-01T12:35:14Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"The Analyst, Data Science (30313) role sits within the Model Risk Management Group (MRMG) under the Global Risk and Compliance organization. The role supports the independent risk management and governance of Generative AI and advanced Machine Learning models across American Express.\nThis role focuses on the assessment and monitoring of LLMs, GenAI applications, and ML-based models used in areas such as marketing, credit, fraud, customer engagement, operations, and risk decisioning. The analyst will contribute to strengthening enterprise model risk controls, elevating model excellence, and supporting compliance with evolving regulatory and governance expectations for AI systems.\nThe role requires strong analytical skills, curiosity in AI/ML technologies, and the ability to translate technical findings into clear, risk-focused insights for stakeholders.\n GenAI Model Risk Assessment & Oversight\nSupport independent oversight and effective challenge of Generative AI, LLM-based, and advanced ML models across the enterprise.\nParticipate in risk based GenAI model risk reviews, including assessment of: Model objectives, design, and architecture\nTraining data, prompt design, and assumptions\nModel performance, monitoring approaches, and control mechanisms\nRisks related to bias, explainability, robustness, and misuse\n\nExecute model risk testing, documentation reviews, and evidence assessment in line with MRMG standards.\nFrameworks, Research & Continuous Learning\nContribute to gap assessments against internal policies and external regulatory expectations for AI/ML models.\nConduct AI/ML and GenAI research to support MRMG guidance, standards, and validation approaches.\nStay current on emerging trends in Generative AI, AI risk management, and regulatory developments, and apply learnings to day-to-day work.\nStakeholder Collaboration & Communication\nPrepare clear, well-structured analysis, validation notes, and risk summaries for internal stakeholders.\nCommunicate analytical findings effectively to business partners, model committees, and senior leaders, with guidance from managers.\nCollaborate with cross-functional teams including data science, engineering, product, and risk partners to support validation execution.\nEnterprise Contribution\nSupport consistent, scalable, and defensible GenAI risk management practices across the enterprise.\nHelp improve efficiency and quality of MRMG processes through strong analytical execution and documentation discipline.\nCritical Factors to Success\nBusiness & Enterprise Outcomes\nContribute to improved model accuracy, robustness, and governance for GenAI and ML models.\nSupport enterprise objectives by enabling responsible AI deployment through strong risk discipline.\nContinuously improve technical and domain expertise to enhance business impact.\nEnterprise Leadership Behaviors\nSet the AgendaDemonstrate enterprise thinking and connect work outputs to broader risk and business priorities.\n\nBring Others With YouCollaborate effectively, seek feedback, and actively contribute as part of high-performing teams.\n\nDo It the Right WayCommunicate clearly and candidly, demonstrate integrity in analysis, and uphold American Express values.\nShow learning agility, curiosity, and willingness to challenge assumptions responsibly.\n\nEducation\nMBA or Master’s Degree in Statistics, Economics, Data Science, AI/ML, Generative AI or related quantitative fields from a top-tier institute.\nExperience\n0-2 years of experience in analytics, data science, model development, validation, or big-data workstreams.\nExposure to AI/ML model development, testing, or validation through professional experience, projects, or internships preferred.\nInterest in or early exposure to Generative AI or LLM-based systems is a strong plus.\nTechnical Skills\nFoundational understanding of AI/ML concepts, with interest in Generative AI technologies.\nHands-on experience with at least one of Python, PySpark, R, or SQL.\nAbility to work with data, perform analytical checks, and support model evaluation activities.\nCore Capabilities\nStrong analytical, problem-solving, and structured-thinking skills.\nClear written and verbal communication, with ability to explain analytical results to diverse audiences.\nAbility to manage multiple tasks, adapt to changing priorities, and meet tight timelines.","description_format":"text","description_chars":4330,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial & Retail Banks","Cards & Card Issuing","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-10-01T01:59:22Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":6,"reasons":["conf:3","velocity","win:early","comp:junior,brand"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/american-express-analyst-data-science-6","json_url":"https://alion.io/job/american-express-analyst-data-science-6.json","meta":{"generated_at":"2026-10-01T16:57:54Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":33,"day_limit":5000,"remaining_today":4967,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}