{"id":3673403,"url":"https://alion.io/job/mastercard-lead-data-scientist-12","title":"Lead Data Scientist","company":{"id":252,"name":"Mastercard","domain":"mastercard.com","url":"https://alion.io/company/mastercard","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":85,"open_postings":173,"ghost_share":0.017,"stale_share":0.503,"repost_share":0.081,"time_to_fill_p50_days":27,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chain-of-Thought","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Hadoop","optional":true},{"name":"LangChain","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-11-03T10:06:24Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-05T14:19:16Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Our Purpose\nMastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.\nTitle and Summary\nLead Data ScientistOur PurposeMastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.\nOverview\nThe Security Solutions Data Science team develops AI and machine learning capabilities that help protect the global payments ecosystem from fraud and cyber threats. Supporting Mastercard’s fraud and risk product, the team builds and enhances models that analyze billions of transactions and enable real-time detection of fraudulent activity.\nWe are looking for a highly motivated Lead Data Scientist to drive the next generation of AI-powered fraud detection systems. This is a senior, hands-on technical role that combines deep individual contribution with technical leadership across predictive modeling, AI agents, LLM-based solutions, real-time decisioning systems, and large-scale data engineering while contributing directly to Mastercard's mission of enabling secure and trusted commerce worldwide.\nRole\nAs a Lead Data Scientist, you will be expected to be highly hands-on while providing technical leadership in designing, developing, coding, testing, and deploying enterprise-scale AI solutions that enhance fraud detection and risk intelligence capabilities across Mastercard's Security Solutions portfolio. This is not just a model development role; it is a strong fit for someone who enjoys working closely with complex data, exploring patterns, understanding the business and technical problem deeply, experimenting through R&D, and bringing interdisciplinary research ideas-such as advances in NLP, deep learning architectures, graph learning, or agentic systems -into the fraud and security space. The role is also about identifying practical solutions and implementing them in scalable production systems.\nYou will lead complex AI engineering initiatives while directly contributing to model development, experimentation, system design, and production implementation.\nKey responsibilities include:\nLead and directly contribute to the design, development, deployment, and optimization of machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.\nDefine, build, and guide scalable AI and ML pipeline architecture supporting model training, validation, deployment, monitoring, retraining, and continuous improvement.\nHands-on design and implementation of Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), reasoning and planning workflows, Chain-of-Thought-style prompting techniques, tool/function calling, LLM-based modular systems, and multi-agent orchestration.\nWork with large-scale transactional datasets using distributed computing technologies such as Spark and Databricks.\nImplement MLOps best practices, including CI/CD, model governance, observability, experimentation frameworks, and performance monitoring.\nDrive the design of reusable AI platforms, libraries, and engineering frameworks that accelerate AI development, improve quality, and establish best practices across teams.\nSet technical direction by evaluating emerging AI technologies, defining adoption patterns, and identifying opportunities to apply them to fraud prevention and security challenges.\nContribute to Mastercard's innovation efforts by publishing top-tier research papers, filing patents, and translating applied AI research into scalable fraud and security solutions.\nEnsure solutions meet Mastercard standards for scalability, reliability, explainability, security, responsible AI, and long-term maintainability.\nAll About You\nDemonstrated experience leading AI Engineering, Machine Learning Engineering, Data Science, or applied research initiatives from concept through production deployment.\nExpert-level hands-on programming skills in Python and strong experience building production-quality software, reusable frameworks, and scalable AI systems.\nDeep understanding of machine learning fundamentals and practical application of supervised learning, ensemble methods, feature engineering, model evaluation, experimentation, and production model risk controls.\nProven track record as a hands-on practitioner developing, deploying, debugging, and improving machine learning solutions in production environments with measurable business impact.\nStrong hands-on experience writing, reviewing, testing, and maintaining production-grade code for AI/ML applications.\nExperience working with LLMs, prompt engineering, reasoning and planning frameworks, RAG architectures, vector databases, AI agents, and Generative AI application development.\nExperience with distributed computing platforms such as Spark, Databricks, or similar big data technologies.\nFamiliarity with cloud platforms such as Azure, AWS, or GCP and associated AI/ML services.\nStrong understanding of MLOps principles, including model deployment, model monitoring, CI/CD, ML lifecycle management, governance, observability, and operational reliability.\nExperience working with SQL and large-scale structured and unstructured datasets.\nStrong analytical thinking and problem-solving abilities with a passion for solving complex real-world challenges.\nExcellent communication, technical leadership, and collaboration skills with the ability to influence architecture decisions and work effectively across cross-functional teams.\nAbility to mentor engineers, raise engineering standards, and bring strong learning agility, intellectual curiosity, and thought leadership in AI and machine learning.\nPreferred Qualifications\nExperience developing fraud detection, risk management, trust & safety, cybersecurity, or financial crime solutions.\nHands-on experience with Hadoop, Spark, Databricks, MLflow, Agentic AI frameworks, reasoning and planning frameworks, LangChain, or similar AI engineering technologies.\nExperience building Agentic AI solutions and multi-agent systems.\nExposure to graph learning, anomaly detection, network intelligence, or real-time decisioning systems.\nPublications, patents, open-source contributions, Kaggle achievements, or demonstrated research experience in AI/ML.\nCorporate Security Responsibility\nAll activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:\nAbide by Mastercard’s security policies and practices;\n\nEnsure the confidentiality and integrity of the information being accessed;\n\nReport any suspected information security violation or breach, and\n\nComplete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.","description_format":"text","description_chars":7637,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Cards & Card Issuing","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-11-03T10:06:24Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely 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