• Analizar grandes volúmenes de datos para identificar patrones, tendencias y oportunidades de negocio. • Diseñar, desarrollar, entrenar y validar modelos de Machine Learning y analítica predictiva. • Preparar, limpiar y transformar datos provenientes de diferentes fuentes para su análisis. • Evaluar el desempeño de los modelos mediante métricas estadísticas y proponer mejoras continuas. • Generar visualizaciones e informes que faciliten la toma de decisiones basada en datos. • Colaborar con equipos de negocio y tecnología para entender requerimientos y convertirlos en soluciones analíticas. • Implementar y monitorear modelos en ambientes productivos cuando sea requerido. • Documentar metodologías, modelos y resultados, asegurando la trazabilidad y reproducibilidad de los desarrollos. • Mantenerse actualizado sobre nuevas técnicas, algoritmos y herramientas relacionadas con ciencia de datos e inteligencia artificial.

• Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization from hypothesis to a validated, monitored result in production • Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty • Design, build, and maintain the data pipelines feeding these models integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization with a bias toward reliability and data quality • Establish and own MLOps practice: model monitoring and drift detection (e.g., population stability), calibration and threshold selection, benchmarking against simple baselines, explainability (e.g., SHAP and interpretable coefficients), experiment and version tracking, and model documentation such as model cards • Partner with subject-matter experts to encode business-rule and heuristic logic alongside statistical models where that produces a more accurate or more defensible result • Gather and document business requirements for revenue-cycle initiatives, including workflows, decision points, stakeholder objectives, operational constraints, success criteria, data availability, and underlying business assumptions, and maintain traceability as those requirements evolve.

Your role at DynatraceDynatrace’s Data & Analytics organization is building a scalable, AI-ready, self-service data ecosystem and standing up a dedicated Data Governance function to define the standards and practices that make data trustworthy and safe for analytics and AI. We treat governance as an enabler, not a gate, a central hub setting companywide standards while partnering with decentralized data owners and stewards. About the Role: This is a foundational hire: an experienced, strategic and collaborative Data Governance Lead to shape the companywide data governance framework. You will define the enterprise standards, processes and operating model for trusted, scalable data and establish the vision and framework for governance. You will partner with engineering to build the platform capabilities that make governance real with reliable data for both analytics and AI, operating within a hub-and-spoke model that sets central standards while decentralized owners, stewards and data product managers apply them. You will help establish AI-ready data and the model for how data and AI can be used responsibly, including the self-service standards that let people and AI tools consume governed data. You will work hands-on while building the practices that scale the function as it grows. This is a high-impact role focused on building trust in data, driving adoption and supporting enterprise decision-making. What You’ll Do: Governance Framework, Standards & Operating Model - Own the enterprise data governance standards, processes and operating model, with input from executive sponsors and data teams - Partner with data product managers who apply governance standards in their workstreams, ensuring portfolio consistency - Build and lead executive and cross-functional councils on standards, priorities and tradeoffs - Refine the data roles model (owners, stewards, custodians) and drive governance adoption and data literacy AI-Ready Data, Self-Service & AI Enablement - Set the readiness criteria and standards that make data AI-ready, structured, documented, high-quality and governed for analytics and AI/LLM use - Establish self-service and data management standards so business users and AI tools can safely consume governed data - Define the standards to enable access to data, how users and AI/agent workflows can reach data, how data is made accessible, with audit trails - Support the AI governance model and partner with security, legal and privacy on AI enablement Data Reliability, Quality & Metadata - Establish the framework for data reliability, including the heightened bar AI and ML demand, with monitoring, scorecards and remediation - Oversee metadata, classification, tagging and lineage across the estate (Snowflake, dbt, Power BI), keeping assets discoverable Platform Partnership, Security & Access Governance - Partner with platform and data engineering to build the capabilities that operationalize governance for secure, reliable data - Define access governance standards (RBAC/RLS, sensitivity labeling) and controls for material non-public information, including insider/MNPI and compliance This is a hybrid role out of our Boston, MA office (2 days per week). What will help you succeedMinimum Requirements: - 10+ years in enterprise data, including 5+ in data governance with demonstrated leadership - Bachelor’s degree in Information Management, Data Science, Business or a related field Preferred Requirements: - Experience with Snowflake, dbt, Power BI and governance tooling - Proven ability to build data governance frameworks, standards and operating models from the ground up - Strong knowledge of governance frameworks (DAMA-DMBOK, DCAM), data management (stewardship, metadata, MDM) and data warehousing/BI - Deep understanding of the data foundations AI requires, quality, lineage, metadata, access control and experience making data AI-ready - Working knowledge of access governance (RBAC/RLS, classification, labeling) and compliance (GDPR, CCPA, SOX, HIPAA) - Proven ability to build and lead executive councils, with strong executive presence and communication Why you will love being a Dynatracer - A one-product software company creating real value for the largest enterprises and millions of end customers globally, striving for a world where software works perfectly. - Working with the latest technologies and at the forefront of innovation in tech on scale; but also, in other areas like marketing, design, or research. - A team that thinks outside the box, welcomes unconventional ideas, and pushes boundaries. - An environment that fosters innovation, enables creative collaboration, and allows you to grow. - A globally unique and tailor-made career development program recognizing your potential, promoting your strengths, and supporting you in achieving your career goals. - A truly international mindset that is being shaped by the diverse personalities, expertise, and backgrounds of our global team. - A relocation team that is eager to help you start your journey to a new country, always there to support and by your side. - Attractive compensation packages and stock purchase options with numerous benefits and advantages. Compensation and RewardsDOE, salary $130K - $160K, plus Health, Dental, Life, STD, LTD, 401K, PTO. Total compensation may vary depending on candidate experience/education and location. Equal Employment OpportunityAll your information will be kept confidential according to EEO guidelines. We offer competitive compensation, company-sponsored premium benefits, medical, dental, vacation/holidays, company matching 401(k) Plan, etc. Dynatrace is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, sex, color, gender identity, religion, national origin, ancestry, citizenship, physical abilities, age, sexual orientation, creed, disability status, veteran status, pregnancy, genetic status, or any other characteristic protected by law. If your disability makes it difficult for you to use this site, please contact careers@dynatrace.com. Dynatrace participates in E-Verify, participant information in English and Spanish. Right to work information in English and Spanish. EEO is the Law. To be considered for this position, please upload your resume/CV.

Role Description - Strong foundation in mathematics: linear algebra, probability, stochastics, optimization theory - Expertise in mathematical programming, algorithm design, and optimization techniques - Skilled in formulating complex problems and designing scalable algorithms (e.g. linear/non-linear programming, convex optimization, combinatorial algorithms, etc.) and experience improving algorithms for efficiency and scalability - Deep knowledge of machine learning, deep learning, and statistical modeling - Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph) - Hands-on experience with neural networks, transformers, diffusion models, or generative modeling - Familiarity with NLP, computer vision, or domain-specific AI applications - Proficient in model evaluation, validation, and performance metrics - Experience with AI/ML frameworks and libraries (e.g. TensorFlow, PyTorch) - Familiarity with software development practices (version control, testing, GPU accelerated computing) - Strong analytical thinking and problem-solving skills - Ability to derive insights and prove algorithmic effectiveness through rigorous logic and math - Demonstrated creativity in tackling open-ended research and real-world AI challenges - Clear communicator, able to translate complex ideas for technical and non-technical audiences - Effective collaborator in cross-functional teams with researchers, engineers, and business partners - Skilled in writing technical documentation, reports, and academic publications is a plus - Passion for AI advancement and continuous learning - Active interest in emerging AI research, with contributions to publications, open-source projects or conference preferred Company Description At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.