Senior Data Scientist to design and build scalable data platforms, pipelines, and cloud solutions that power global pricing decisions.
Compensation
Pay for performance -Annual incentive Bonus plan.
Responsibilities
Build infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability
Perform debugging, troubleshooting, modifications and testing of integration solutions
Operationalize the developed jobs, processes and models
Create databases and infrastructure to process data at scale
Lead end‑to‑end design, development, and deployment of advanced machine learning models and AI‑driven products in healthcare.
Responsibilities
Lead the end‑to‑end design, development, and deployment of advanced machine learning models and AI‑driven products that deliver significant business value.
Act as the technical lead on complex data science projects, defining the roadmap, setting priorities, and ensuring the scientific rigor of the team's work.
Architect and implement scalable, production‑ready data science pipelines and MLOps frameworks in collaboration with engineering teams.
Serve as a primary consultant to executive leadership and business stakeholders, translating complex data insights into strategic recommendations.
Mentor and develop other data scientists and engineers, fostering a culture of innovation, collaboration, and continuous technical growth.
Requirements
Master’s degree or PhD in a quantitative field such as Computer Science, Data Science, Statistics, Physics, or a related discipline
Lead the end-to-end machine learning lifecycle, from problem framing to model deployment and monitoring, while collaborating with cross-functional teams and mentoring peers.
Responsibilities
Own the ML lifecycle: take models from problem framing through training, deployment and monitoring
Frame problems: turn business goals into ML tasks and metrics
Build models: classical ML, time series and NLP, plus LLM and RAG features
Evaluate rigorously: sound validation, the right metrics, and evals that gate every release
Ship and run models: deploy with Docker and CI/CD on cloud ML platforms, and monitor drift and cost
Architecte Data / IA Services Financiers responsable de la définition, industrialisation et déploiement d’architectures Data & IA pour les services financiers, en assurant conformité réglementaire et performance.
Compensation
Rémunération : En fonction du profil
Responsibilities
Définir des architectures Data & IA : data mesh, feature stores, plateformes MLOps, scoring, pricing et IA générative
Industrialiser les solutions en garantissant passage à l’échelle, performance, résilience et maîtrise des coûts
Traduire les exigences de l’AI Act, DORA, ACPR, RGPD, Bâle et Solvency en choix de conception
Garantir l’explicabilité, la traçabilité, la robustesse et le contrôle des biais des modèles
Intégrer les contraintes des métiers : risques, conformité, anti-fraude, KYC/AML, marketing, relation client, actuariat et trading
Requirements
Master's degree (Bac+5) in Engineering or Business School with strong technical specialization
The DevOps Engineer will work within the DevOps Engineering team to support various Company engineering teams in their efforts to develop, build, test, and deploy their solutions to the cloud.
Locations (3)
Chicago, Illinois, United States
Irving, Texas, United States
Broomfield, Colorado, United States
Compensation
Estimated Duration (In Months): 13
Responsibilities
Perform all programming, project management, and development assignments without close supervision
Works directly on complex application/technical problem identification and resolution, including responding to off-shift and weekend support calls
Works independently on complex systems or infrastructure components that may be used by one or more applications or systems
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