Senior Applied Scientist, Operations Research

HALLO! Our goal at Data4Life is to make the world a healthier place. Our Product and Project Management team makes sure we move toward that goal with focus, rigor and speed -translating research ambition into software that generates real impact in healthcare, in Germany and beyond. We're looking for a Senior Project Manager to lead the projects of our Impact Program - Data4Life's flagship partnership initiative for advancing real-world evidence generation across healthcare. The Impact Program runs in successive calls: individual partnerships with early-career researchers, solutions partnerships co-developing in therapeutic areas like oncology, neurology, and cardiovascular disease, and data partnerships building federated multi-site networks. The first call, Collect for Impact, currently funds projects across the globe -spanning topics like post-stroke rehabilitation and multi-wearable research. The second and third calls are in active preparation. In this role, you'll do more than shepherd these projects. As a tech company, Data4Life contributes through software development -and every Impact Program project is an applied research instance that should sharpen our platforms: Data2Evidence, our RWE platform built on the OMOP Common Data Model, and D4L Collect, our sensor data collection platform for digital health studies. You'll work closely with our Scientific Evidence Group on protocol refinement, analysis, and manuscripts; with engineering and product on platform development; and with our partners and the Hasso Plattner Foundation on program-level reporting. This is a full-time position based in Potsdam / Berlin. You'll report to the Head of Product and Project Management and partner across software engineering, scientific evidence, design, legal, and business development teams, as well as with international partners spanning academic medical centers, public health systems, and foundations. DEINE ZUKÜNFTIGE ROLLE * Lead the end-to-end delivery of the Impact Program portfolio, from call design coordination and applicant selection through project kickoff, execution, and dissemination, across the program's three streams (individual, solutions, and data partnerships). * Drive cross-functional delivery for each funded project across software engineering, design, the Scientific Evidence Group, and our partners -aligning Scrum and Kanban delivery cadences with the milestone-driven timelines typical of clinical and academic research programs. * Translate what each Impact Program project teaches us back into platform evolution. Surface concrete signals into the Data2Evidence and D4L Collect roadmaps -new sensors, new languages, new data integrations, new validation needs- such that our platforms strengthen through the program's work. * Partner with awardees as collaborators and mentees. Help early-career researchers and senior investigators refine study protocols, scope feasible analyses, navigate Data4Life's infrastructure, and ship publications and open datasets that strengthen their careers and our visibility. * Coordinate with the Scientific Evidence Group on protocol refinement, analysis feedback, and manuscript contributions -bringing rigor and pace to the scientific deliverables without becoming the bottleneck. * Own the program's operating model within Data4Life: planning, risk and dependency tracking, decision and assumptions logs, and reporting cadences for leadership, the Hasso Plattner Foundation, and external partners. * Communicate progress as impact -turning scientific and engineering milestones into stories that resonate with funders, clinicians, the research community, and the public. * Help mature the Impact Program across successive calls. Each call sharpens selection criteria, the support model, and what the program delivers for awardees and for Data4Life's mission. WAS DU FÜR DEINEN ERFOLG BRAUCHST * 4+ years of project or program management experience, with at least 2 of those years managing scientific, research, or academic projects. * Hands-on experience coordinating projects in a software development context, comfortable with software delivery cadences, technical scoping conversations, and how product and engineering decisions affect a research project's success. * Demonstrated experience working with academic researchers, principal investigators, and early-career scientists, including navigation of IRB/ethics approval processes, study protocol refinement, and the rhythms of academic timelines. * Experience managing projects across time zones, cultures, and organization types (academic medical centers, public health systems, foundations, NGOs, industry partners). * Working knowledge of managing sensitive patient and research data under GDPR; familiarity with HIPAA where relevant. * Scientific literacy in health-adjacent domains: enough to engage substantively with study protocols, statistical analysis plans, and manuscript outlines, even when you're not the primary author. * Familiarity with open science practices: FAIR data principles, open-access publication, public code and data repositories (e.g., Zenodo, GitHub), and reproducibility-aware analysis workflows. * Mastery of Scrum or Kanban, with the flexibility to interface with the milestone-driven, often waterfall-flavored timelines common in clinical and academic research. * Strong leadership and mentoring skills in a multidisciplinary, multi-stakeholder research environment. * A Bachelor's degree in Life Sciences, Public Health, Biomedical Engineering, Health Informatics, Computer Science, or a related field, or equivalent professional experience managing complex research or software projects. * Languages: You're fluent in English. German at B2 level is a plus; a third language is a plus. Nice to have: * A Master's degree, PhD, or MD in a relevant field; or a PMP, Certified Scrum Master (CSM), or related project management certification. * Working understanding of real-world evidence concepts and standards, the OMOP Common Data Model and OHDSI tooling, HL7 FHIR, and CDISC. * Prior experience running a grants program, research network, or foundation-funded research portfolio. * Personal scientific output (publications, datasets, conference contributions) demonstrating scientific maturity. DAS ERWARTET DICH BEI UNS * High level of personal responsibility and creative freedom * Work on socially impactful projects in the healthcare sector * Collaboration with leading partners from research, technology, and healthcare * A diverse, interdisciplinary, and international team working across three locations (Potsdam, Berlin, and Singapore) * Flexible working models with the option to work from home * Permanent employment contract with 30 days of vacation * Attractive employee benefits, e.g. private supplementary health insurance, public transport ticket GUT ZU WISSEN * We especially welcome applicants from the LGBTQI+ community, people with a migration background, people of color, and individuals with neurodiversity, disabilities, or chronic illnesses to the team. * If you need support or special assistance during the application or interview process due to a disability, please feel free to let us know and we are happy to accommodate you as much as possible. * Please enclose your complete documents (CV, Github Account, Work sample) with your application. * For this position it is important that you are able to regularly be present in our Potsdam and/or Berlin office.

Vestiaire Collective is the leading global online marketplace for desirable pre-loved fashion. Our mission is to transform the fashion industry for a more sustainable future by empowering our community to promote the circular fashion movement. Vestiaire was founded in 2009 and is headquartered in Paris with offices in London, Berlin, New York, Singapore, Ho Chi Minh, and warehouses in Tourcoing (France), Crawley (UK), Hong Kong and New York. We currently have a diverse global team of 600 employees representing more than 50 nationalities. Our values are Activism, Transparency, Dedication and Greatness and Collective. About the Role We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective. While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace. You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies. Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact. What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code. You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication, immediately improving our trust and safety ecosystem. Mid-Term Foundation (MLOps Lifecycle & Infrastructure): Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring. You will scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations), design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience. Long-Term Vision (Centralizing 360-Degree MLE Capabilities): Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization. You will transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms. Who You Are Must-Haves: Experience: 5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems. Production Infrastructure: Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow). Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure. MLOps & Pipelines: Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles). You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases. Feature Stores: Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment. Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued. Strategic Builder Mindset: You are an analytical builder who thinks long-term. You can successfully evaluate TCO for bespoke internal systems versus enterprise tools, anticipate technical liabilities, and design robust architectures that handle unpredictable peak traffic surges. Collaboration & Engineering Hygiene: Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution. Nice-to-Haves: Relevant Domain Expertise: Background in E-commerce, Single-SKU Marketplaces, Search & Recommendation, Trust & Safety, or Counterfeit Detection. Vision, Edge & Optimization: Hands-on experience with Vector Databases, Visual RAG pipelines, deploying Deep Learning VLM models, and optimizing models for edge computing or low-latency inference (e.g., ONNX, TensorRT). Infrastructure & Observability: Advanced experience with containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), and data transformation workflows (dbt). Familiarity with setting up advanced monitoring for model performance, concept drift, and system health (Datadog, Prometheus).