• Design, develop, and deploy machine learning models, including LLMs, transformer-based models, and traditional ML approaches • Build and optimize NLP, recommendation systems, and predictive analytics solutions to address complex business problems • Develop and implement RAG (Retrieval-Augmented Generation), prompt engineering strategies, and emerging agentic AI workflows • Conduct rigorous model evaluation using appropriate statistical methods and performance metrics • Acquire, integrate, and preprocess structured and unstructured data from diverse sources (e.g., relational databases, NoSQL systems, logs, external datasets) • Perform feature engineering and data preparation to ensure high-quality inputs for ML models • Ensure data quality, validation, and governance compliance for AI/ML use cases • Design and maintain scalable data and ML pipelines to support model training, evaluation, deployment, and monitoring • Deploy models into production using cloud-native architectures and APIs • Implement model monitoring, drift detection, and retraining strategies to ensure sustained performance • Contribute to CI/CD pipelines and ML lifecycle automation • Contribute to system architecture decisions across data, ML, and cloud platforms • Provide technical leadership in selecting tools, frameworks, and design patterns for AI/ML solutions • Mentor junior team members and promote best practices in data science and ML engineering • Partner with stakeholders, product owners, and subject matter experts to translate business needs into AI/ML solutions • Communicate findings, insights, and recommendations clearly to both technical and non-technical audiences • Deliver solutions that drive measurable improvements in efficiency, quality, and business outcomes.

• Define and implement a multi-year enterprise data strategy aligned to business, clinical, and technology objectives. • Serve as the executive owner for data platforms, data governance, metadata strategy, and enterprise data quality. • Oversee modern, scalable data platform architecture (e.g., ingestion, transformation, consumption layers). • Modernize workloads using platforms such as Databricks and improve data orchestration, monitoring, observability, and automation. • Ensure standardized, business-ready “gold‑layer” datasets to support analytics and data science. • Consolidate enterprise reporting under a unified model with consistent definitions, governed metrics, and repeatable processes. • Partner with business leaders to deliver actionable analytics insights tied to operational, clinical, and financial KPIs. • Maximize performance, consistency, and usability of dashboards and analytical assets. • Establish and lead a data governance framework covering quality, lineage, security, and stewardship. • Ensure compliance with regulatory, privacy, and security standards, including evolving AI-related governance expectations. • Provide platforms, standards, and data quality foundations required for scalable, trustworthy machine‑learning and AI adoption. • Partner with Data Science leadership to ensure strong alignment on data needs and lifecycle processes. • Build and develop a high-performing team across data engineering, analytics, governance, and architecture. • Foster a culture of accountability, innovation, and continuous improvement. • Provide clear operating models for how data teams partner with technology and business units.

• Work with partner ML and Annotation engineers and TPMs to specify data and training requirements • Ensure that datasets are correctly curated, filtered, and processed to meet the rigorous standards of production-level machine learning • Act as a technical translator, turning high-level project goals into actionable data specifications for engineering and annotation teams • Maintain high standards for data integrity, ensuring that the media datasets used for training are representative and accurate

Role Description Rain is the world's first AI Financial Health Platform, serving 3.5 million employees at leading organizations like McDonald's, Marriott, and T-Mobile. Rain works in the background to optimize every employee's financial life to prevent shortfalls and build long-term stability. About the Team: Our data science team sits at the center of Rain's product. We're a small, senior team embedded in a fast-moving fintech, which means the models we build go directly into production decisions — credit risk scoring, balance forecasting, personalized financial insights — and the impact is immediate and measurable. We work closely with product, engineering, and compliance, and we operate like owners: defining problems, building solutions, and monitoring them in production. If you're the kind of data scientist who gets energized by seeing your work move the needle on a real product — not just a dashboard — this team was built for you. This role is based remotely in EMEA. You'll be a key early hire on our international data science presence, working across time zones with our U.S.-based team and contributing to how we scale our ML function globally. What You’ll Do - Run end-to-end experiments: feature engineering, model selection, A/B testing, and production monitoring - Build and maintain scalable, well-documented pipelines that keep models healthy in production - Design, train, and deploy ML and Agentic models that drive core product decisions, including credit risk, forecasting, and personalized recommendations - Collaborate with product and engineering to translate business problems into well-scoped modeling tasks - Communicate model behavior and findings to both technical and non-technical stakeholders Who You Are - You thrive in ambiguity — you can take a loosely defined business problem, ask the right questions, and turn it into a well-scoped modeling task without waiting for a perfect brief - You are a strong cross-functional collaborator who builds trust with product, engineering, and compliance partners and can speak their language - You have a bias toward shipping — you know when a model is good enough to get into production and how to iterate from there, rather than optimizing in isolation - You take ownership end-to-end: from a messy raw dataset to a monitored production model, you don't hand things off and walk away - You communicate with clarity — you can walk a skeptical stakeholder through a model's tradeoffs without leaning on jargon - You care deeply about model behavior in the real world, not just on a held-out test set - You mentor and elevate the people around you, and you're energized by working somewhere where the stakes are real Qualifications - Python and core ML libraries (pandas, scikit-learn, PyTorch, or TensorFlow) - SQL and working with large, complex datasets - Experience with LLMs and NLP techniques (fine-tuning, RAG, prompt engineering or similar) - Communication skills to explain models trade offs - Solid understanding of statistical modeling, experimentation, and model evaluation - Experience taking models from prototype to production - Familiarity with agentic frameworks (e.g. Langchain) and agent orchestration and evaluation Diversity, Equity and Inclusion Commitments As part of our dedication to the diversity of our workforce, Rain is committed to Equal Employment Opportunity and does not discriminate based on race, religion, color, national origin, ethnicity, gender, sex (including pregnancy), protected veteran status, age, disability, sexual orientation, gender identity, gender expression, or any unlawful criterion existing under applicable federal, state, or local laws. If you need assistance or accommodation due to a disability, you may contact us at HR-US@rain.us. What’s Next Ensuring a smooth and enjoyable candidate experience is critical for us. Our interview process tends to take about 4 weeks to complete, but may fluctuate depending on the role. Learn more about our hiring process here. Don’t be afraid to let us know if you need more flexibility.