• Profile the anonymized lake hands-on — interrogate tens-of-millions-of-row tables and reproduce and validate the team's existing descriptive statistics, so every number is traceable to source. • Build and validate the core risk models yourself: PD, delinquency / roll-rate, early-warning, segmentation and scorecards. • Stand up the model-validation discipline that makes outputs audit-defensible: train / test / out-of-time splits, Gini / AUC / KS, calibration, stability (PSI), backtesting and full model documentation. • Define feature logic with the Data Engineer and write it yourself in SQL / dbt / Python; specify the harmonized definitions the semantic layer must serve. • Prototype and validate the natural-language insight layer; check answer correctness and add guardrails. • Run a credit-policy / cut-off analysis showing where the client could tighten policy or reduce delinquency — the concrete insight their own clients keep asking for. • Lead a small pod (Data Engineer, client's junior offshore data people): set tasks, review work, be the quality bar and the human-in-the-loop. • Front the client's data leadership: present findings, explain methodology to non-technical executives, and shape the phased roadmap / SoW.

Role Description Design, develop, and program methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for client services and product enhancement. - Interact with product and service teams to identify questions and issues for data analysis and experiments. - Develop and code software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources. - Identify meaningful insights from large data and metadata sources; interpret and communicate insights and findings from analysis and experiments to product, service, and business managers. - May telecommute. Qualifications - Bachelor’s degree in Computer Science, Engineering, Mathematics, or related technical field. - 8 years of progressive, post-baccalaureate experience in the job offered or in a data scientist-related occupation. Requirements - Mathematics using principles including Calculus, Linear Algebra, Graph Theory, and Discrete Math. - Probability and statistics using principles including probability distribution, standard deviation, Bayesian and frequentist Statistics, and Dimension Reduction. - Programming languages including Python, SQL, Java, and Scala. - Tools including PySpark, Keras, Scikit-Learn, Pandas, Tensorflow, and Huggingface. - Time-Series Analysis. - Natural Language Processing (NLP). - A/B Testing. - Machine Learning. - Predictive Data Analytics. Benefits - Flexible medical, life insurance, and retirement options. - Opportunities for community involvement through volunteer programs. Company Description Only Oracle brings together the data, infrastructure, applications, and expertise to power everything from industry innovations to life-saving care. With AI embedded across our products and services, we help customers turn that promise into a better future for all. True innovation starts when everyone is empowered to contribute. We’re committed to growing a workforce that promotes opportunities for all. Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law.

• Build predictive and statistical models on real-world healthcare data, with guidance from senior data scientists and clinical input • Help craft robust, production-ready models and pipelines that meet business and clinical objectives • Support the design and analysis of A/B tests and studies, validating results against clinical reality • Acquire, clean, and transform large, messy clinical and claims datasets using SQL and modern data tooling • Partner closely with our Clinical team and clinical SMEs to understand workflows, define labels, and validate that models make clinical sense • Craft clear visualizations, reports, and presentations that turn analysis into action for clinical and business audiences

• Diseñar y construir pipelines de datos que alimenten los flujos de entrenamiento e inferencia de ML. • Desarrollar, evaluar e iterar modelos de machine learning y estadísticos orientados a soluciones de negocio. • Gestionar el ciclo de vida completo del modelo: desarrollo, validación, despliegue y monitoreo. • Colaborar con stakeholders para traducir problemas complejos de negocio en soluciones funcionales de ciencia de datos. • Documentar el trabajo con claridad suficiente para que pueda ser mantenido y extendido por el equipo. • Desplegar modelos en entornos de producción garantizando confiabilidad, rendimiento y mantenimiento del sistema.