{"id":63814,"url":"https://alion.io/job/mastercard-lead-data-scientist-ai-engineering","title":"Lead Data Scientist, AI Engineering","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":94,"open_postings":221,"ghost_share":0.018,"stale_share":0.357,"repost_share":0.09,"time_to_fill_p50_days":21,"computed_at":"2026-10-10T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Dublin, Ireland"],"countries":["IE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":105000,"max_usd":252000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1048},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-08-19T00:00:00Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-11T19:39:23Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":53},"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 Scientist, AI EngineeringLead Data Scientist, AI EngineeringOverview\nMastercard's AI Centre of Excellence is building the next generation of AI capabilities powered by large-scale transaction data, machine learning, and foundation models. We are transforming how AI solutions are developed by enabling teams to leverage reusable learned intelligence rather than building bespoke feature-engineering pipelines for every use case.\nWe are seeking a Lead Data Scientist, AI Engineering to lead the development of advanced machine learning solutions across domains. This role combines deep expertise in predictive modelling, experimentation, and technical leadership to deliver measurable business impact.\nWhat You'll Work On\nThis role focuses on applying machine learning, predictive modelling, and foundation-model representations to solve business problems at scale. Typical use cases include forecasting, propensity modelling, recommendation systems, behavioural analytics, and customer intelligence.\nWhile familiarity with Generative AI is beneficial, this is primarily an applied machine learning and data science leadership role rather than a conversational AI, RAG, or agentic systems engineering position.\nRole / Key Responsibilities\nLead the design, development, and deployment of machine learning solutions that solve high-impact business problems.\nDefine modelling approaches, experimentation frameworks, and success metrics for AI initiatives.\nApply foundation-model embeddings and modern machine learning techniques to improve model performance and accelerate development.\nDrive projects from problem definition through model deployment and business impact measurement.\nEstablish robust evaluation frameworks and benchmark new approaches against existing solutions.\nPartner with business, product, engineering, and analytics teams to identify and prioritise opportunities.\nPresent technical findings and recommendations to stakeholders and senior leadership.\nMentor and develop data scientists and AI engineers through technical guidance, reviews, and coaching.\nContribute to hiring, capability development, and the long-term technical direction of the AI organisation.\nAll About You\nRequired Experience\nProven experience leading machine learning projects from concept through production deployment.\nExperience solving predictive modelling problems such as attrition, forecasting, recommendation systems, propensity modelling, fraud detection, risk modelling, or customer analytics.\nStrong track record of delivering measurable business outcomes through machine learning.\nExperience leading technical teams, mentoring practitioners, and influencing technical direction.\nRequired Technical Skills\nStrong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.\nAdvanced Python and SQL skills.\nExperience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.\nStrong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.\nExperience with feature engineering, representation learning, embeddings, and downstream machine learning workflows.\nFamiliarity with transformer-based models and foundation-model applications.\nExperience working with Databricks, Spark, Azure, AWS, or GCP.\nLeadership & Communication\nStrong problem-solving and decision-making skills.\nAbility to lead through influence across cross-functional teams.\nExcellent communication and stakeholder management capabilities.\nAbility to translate complex technical concepts into actionable business insights.\nMinimum Qualifications\nBachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.\n8+ years of experience in machine learning, data science, AI, or advanced analytics.\nExperience developing and deploying machine learning models in production environments.\nExperience leading technical projects or teams.\nPreferred Qualifications\nMaster's degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Mathematics, or a related field.\nExperience with foundation models, embeddings, or representation learning.\nExperience in financial services, payments, banking, fintech, fraud, marketing analytics, or customer intelligence.\nPublications, patents, conference presentations, or other evidence of technical thought leadership.\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":5580,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Cards & Card Issuing","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-08-19T00:00:00Z"},{"event":"close","at":"2026-08-20T07:56:50Z"},{"event":"reopen","at":"2026-09-10T13:18:55Z"}],"visa":[],"liveness":{"score":15,"band":"cold","label":"Long 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