• 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.

Role Description What you’ll do: - Combine deep understanding of machine learning, advanced data analysis, statistical testing, and the ability to communicate insights effectively to both technical and non-technical stakeholders. - Operate as a full stack data scientist utilizing expertise in both machine learning and cloud technologies, while also growing and leading a team. - Lead data science projects through development lifecycle including offline evaluation, deployment, experimentation, feedback, and model improvements. - Formalize procedures about how models are expected to behave, creating definitions of outliers, developing methods to identify them systematically, and explaining why they are reasonable or identifying fixes. - Design, develop, and implement machine learning models for various applications, including but not limited to predictive analytics, natural language processing, generative AI, and computer vision for different retail applications. - Research and implement state-of-the-art machine learning algorithms to address specific business challenges. - Analyze large, complex data sets to extract actionable insights and identify trends, patterns, and opportunities. - Handle data collection, cleansing, and validation processes to ensure data integrity and reliability. - Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand requirements and deliver effective solutions. - Document code, algorithms, and processes to facilitate knowledge sharing and ensure reproducibility. - Provide technical guidance in best practices for model development to the data science team. Qualifications - Master’s degree in Data Science, Math, Statistics, Computer Science, or related field and 4 years of experience as a Computer and Information Systems Manager or a related position/occupation. - Ph.D. degree in Data Science, Math, Statistics, Computer Science, or related field and 2 years of experience as a Computer and Information Systems Manager or a related position/occupation. Requirements - Apply advanced mathematics and data science methodologies. - Standard machine learning and statistical techniques including predictive models (time series, regression, etc.), classification, forecasting. - Programming in Python and ML frameworks. - Developing and deploying advanced machine learning models and algorithms in a production environment. Benefits - Base salary range: $189,684 to $296,000 per year. - Eligible for 401k and a new hire and annual equity grant. - C08+ positions may also be eligible for annual bonus. - Different types of insurance and benefits, such as medical/Rx, vision, dental, life, disability, hospital indemnity, critical illness, and accident. - Parental leave, family services benefits, backup dependent care, flexible spending accounts, telemedicine, pet adoption reimbursement, employee assistance program. - Discounts including 10% off pet insurance and 20% off at Chewy.com. - Unlimited PTO for exempt salary team members, subject to manager approval. - Six paid holidays per year. - Eligibility for paid sick and family leave in compliance with applicable state and local regulations.