{"id":191110,"url":"https://alion.io/job/mastercard-data-scientist-i-2","title":"Data Scientist I","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":254,"ghost_share":0.016,"stale_share":0.358,"repost_share":0.094,"time_to_fill_p50_days":21,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13500,"max_usd":30000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Databricks","optional":false},{"name":"Diffusion Models","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-09-02T14:51:05Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-18T07:19:54Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":45},"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\nData Scientist IWho is Mastercard?Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.\nOur decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.\nOverview\nGlobal Risk and Compliance contributes towards the risk and compliance portfolios of Mastercard. Compliance program works towards Transaction Monitoring in the fields of Anti Money Laundering, Regulatory compliance requirements etc. We are looking at implementing an AI based solution to support increased transaction monitoring for AML activities. If you are the one who enjoys solving problems in a challenging environment, and who has the desire to take their career to the next level. If any of these opportunities excite you, we would love to talk.\nRole\nImplement generative AI models and applications for AML transaction monitoring use cases.\nBuild advanced solutions leveraging Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), tool calling, and LangChain/LangGraph for dynamic workflows.\nDevelop machine learning pipelines for fine-tuning large language models (LLMs) and other foundation models.\nImplement content generation systems for text, image, and multimodal outputs.\nApply computer vision techniques for image analysis, creative generation, and visual search.\nImplement evaluation frameworks for GenAI applications, including hallucination detection, bias checks, and quality scoring.\nBuild observability and monitoring solutions for AI systems, including latency, cost tracking, and model performance metrics.\nBuild scalable solutions on cloud platforms (AWS, Azure) and leverage data platforms like Databricks.\nIntegrate AI models into production systems with a focus on performance, security, and compliance.\nStay current with the latest advancements in AI/ML research, particularly in generative AI, and apply them to real-world problems.\nPromote engineering best practices and contribute to a culture of innovation and collaboration.\nAll About You\nHas strong knowledge in building and deploying generative AI solutions (e.g., LLMs, diffusion models, transformers).\nExpertise in RAG pipelines, MCP-based integrations, tool calling frameworks, and LangChain/LangGraph.\nProficiency in Python and popular AI/ML frameworks such as PyTorch or TensorFlow.\nExperience with content generation systems (text, image, multimodal) and computer vision models.\nExposure to evaluation techniques for GenAI (e.g., hallucination detection, factuality scoring, bias evaluation).\nKnowledge of observability tools for AI systems (e.g., monitoring latency, cost, and performance metrics).\nExposure to MLOps practices, including model versioning, CI/CD for ML, and monitoring in production.\nExposure to cloud platforms (AWS, Azure) and data engineering tools like Databricks.\nSolid understanding of prompt engineering, fine-tuning, and model evaluation techniques.\nKnowledge of API development and integration of AI models into web or mobile applications.\nStrong problem-solving skills and ability to work in a fast-paced, high-impact environment.\nExcellent communication and collaboration skills to work effectively with technical and non-technical stakeholders.\nEducation:\n• Bachelor or Master's Degree in Computer Science or equivalent\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 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