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.

Role Description Pierre Fabre Laboratories are hiring a highly skilled and experienced Lead Statistician – Real World Evidence (RWE) to join the Biometry Department, part of the Data Science & Biometry Department, based in Toulouse (Oncopôle) or Boulogne. This position can be filled remotely. This position requires a strong expertise in the application of statistics within the context of Real-World Evidence studies. The successful candidate will be agile and adaptable, capable of working in a matrix-organized environment where collaboration across Data Management, Programming, and Statistics is essential. Whilst an excellent track record in RWE is a must, the successful candidate will also contribute to clinical trials, including randomized controlled trials (RCTs). The Biometry Department at Pierre Fabre supports all drugs being developed by the company. The department encompasses Data Management, Programming, and Statistics, working cohesively to ensure the successful completion of both Randomized Controlled trials and Real-World evidence studies. Your role within a pioneering company in full expansion: - Participate in the conception and design of Real-World Evidence studies, providing expert statistical support including study design, sample size determination, definition of study objectives and evaluation criteria, writing the statistical section of the protocol and development of the statistical analysis plan. - Act as the primary point of contact internally and externally for all statistical aspects of assigned projects and studies, attending project/study meetings and offering statistical guidance. - Contribute to the selection and evaluation of subcontractors, establishing clear requirements and evaluating proposals. - Draft and/or validate key study documents such as synopses, protocols, CRFs, data review plans, statistical analysis plans, statistical results, study reports and other study-level documents while ensuring timely deliverables. - Program and/or validate statistical analyses using SAS software. - Collaborate with project team and clinicians to interpret results, develop key messages, and contribute to scientific publications. - Work with clinicians and medical writers to communicate findings to clinical and regulatory partners, prepare Response to Questions from regulatory agencies (EMA / FDA / PMDA / …) and prepare summaries of results for regulatory documents. - Contribute to the RWE strategy for the submission dossier to FDA and EMA. - Oversee the review of statistical documents within the team and supervise operational activities outsourced to CROs ensuring adherence to cost, timeline, and data quality requirements. - Follow all industry standards, including ensuring that all statistical related documentation is included in the electronic Trial Master File (eTMF). Qualifications - Advanced degree in Statistics (Master's Degree, ENSAI, ISUP, or PhD) or a related field. - A minimum of 6 years of experience in the pharmaceutical industry or a Contract Research Organization (CRO), with significant involvement in Real World Evidence studies from the outset. Requirements - Advanced statistical analysis skills. - Proficiency in SAS software. - Strong understanding of real-world evidence studies design and methodology. - Familiarity with the specific methodologies and regulations associated with Real-World evidence studies. - Comprehensive understanding of both primary data collection and secondary data re-utilization in studies. - Familiarity with regulatory guidelines (ICH, EMA, FDA, NICE, ENcEPP). - Excellent written and verbal communication skills in English. - Excellent communication and cooperation skills, with a rigorous and pragmatic approach. - Creativity, proactivity, rigor, autonomy, and collaborative spirit. - Strong ability to manage multiple studies simultaneously and maintain organization. Optional Skills - Experience with interventional studies. - Knowledge of CDISC standards. Benefits - Attractive remuneration/benefits package. - Incentives and profit-sharing. - Pierre Fabre shareholding with matching contribution. - Health and provident insurance. - 16 days of holidays (RTT) in addition to 25 days of personal holidays. - Public transport participation. - CE (Comité d'Entreprise) benefits. Terms of Employment - Full-time position. - Work location: full remotely or hybrid with 2 days per week from home. Application Process Interested candidates should submit their resume and a cover letter detailing their experience and qualifications for this position. The hiring manager is Florence Carrère, Biometry Manager. The Head of the Biometry Department is Guillaume Desachy. We look forward to welcoming a new member to our dedicated and innovative team at Pierre Fabre.

• Drive deep-dive investigations into KPI shifts, going beyond surface-level reporting to uncover root causes and hidden trends• Lead and mentor a team of Brand Analysts, developing them from data reporters into strategic co-owners of their respective brands• Maintain 360-degree oversight of the entire business ecosystem, monitoring how acquisition, CRM, and fraud initiatives impact each other• Bridge business and data science teams, translating complex commercial needs into technical requirements for ML models• Create innovative segmentation strategies that enhance predictive model accuracy for churn, LTV, and propensity modeling• Act as the final gatekeeper for brand health, ensuring growth in one area doesn't compromise performance elsewhere