Senior / Mid - Data Scientist - NYC

About the role

Aily Labs is a B2B SaaS company building an AI-powered decision intelligence app for enterprises. Our mobile-first platform combines company data with advanced machine learning, large language models and agentic AI to give business leaders clear, actionable insights in real time. Instead of traditional dashboards and reports, Aily delivers simple, personalized recommendations that help teams decide and act faster across finance, supply chain, R&D, commercial, and more. The app is designed to be intuitive for end users and easy to integrate for IT, so companies can move from analysis and indecision to confident, data-driven decisions every day. Founded in 2020 in Munich, Aily Labs has grown into a global team of 300+ people across Munich, Barcelona, Madrid, Cluj, and New York. Our mission is to democratize AI in business and enable leaders to Lead Boldly , turning complex data into decisions that drive real, measurable impact. We are looking for Data Scientists who are passionate about the power of AI and who thrive at the intersection of business, analytics, and AI: someone who not only trains models but also deeply understands the business context in which they're applied. Your job is to make sure AI is solving the right problems, using the right approaches, and driving measurable impact. About this role - Domains Department The Data Science Domains Department powers business-critical decision-making across four key areas of enterprise operations. We build and deploy AI-driven features that transform how business leaders understand and act on complex data challenges in their specific domains. Each domain has distinct requirements, challenges, and opportunities-but all share the mission of delivering real-time, actionable AI insights that drive measurable business impact. Our Four Domains: Finance (FIN): We enable smarter, faster financial decision-making across the enterprise by partnering with Finance teams to improve forecasting accuracy, planning agility, and financial transparency through AI-driven solutions. We tackle volatile revenue and demand signals, large-scale multi-dimensional forecasting, and resource allocation under uncertainty, while ensuring models remain explainable and actionable. Our work moves Finance beyond reactive reporting toward proactive data-driven decision-making, powered by production-grade ML systems at enterprise scale. Manufacturing & Supply Chain (M&S): We optimize the end-to-end manufacturing and supply chain with cutting-edge AI. Our team works closely with supply chain champions across Fortune 500 companies to drive impact: from helping pharmaceutical companies to avoid out-of-stocks to reducing machine breakdown in consumer good factories. Our solutions consist of a mix of statistical, machine learning, GenAI, Knowledge Graph, and agentic techniques to tackle challenges such as low signal demand forecasting, explainability for supply chain autonomy, and providing detailed actionable recommendations based on a mix of textual, image, and structured data. Research & Development (R&D): We are passionate about clinical operations and portfolio management. Our team partners closely with pharmaceutical and biotech companies to deliver innovative AI-driven solutions that optimize clinical trial recruitment and accelerate pipeline innovation. Some of the exciting use cases we've worked on include predicting patient enrollment, optimizing trial design, building knowledge graphs across the pharma ecosystem, and more. Cross-Functional (XF): this team addresses critical business areas that extend beyond the scope of our other specialized domains. We apply AI to diverse functions spanning procurement, people analytics, commercial operations, and more - developing solutions that transform these essential but often underserved areas. By focusing on these complementary business functions, we ensure our AI platform delivers comprehensive value across the entire enterprise, helping organizations unlock insights and efficiencies in every corner of their operations. Core responsibilities and skills (by seniority) Mid Data Scientist - 2-4 years of experience Design and implement end-to-end data pipelines (ingestion → transformation → quality → delivery) for multiple use cases with growing autonomy. Build and maintain data infrastructure components (streaming pipelines, transformations, APIs, catalogs) using modern data tools. Implement robust data quality frameworks, monitoring, and alerting systems at scale. Optimize data workflows for cost, performance,e and reliability across multiple datasets. Mentor junior team members on data engineering best practices and tooling. Senior/Lead Data Scientist - 4+ years All of the above + Have comprehensive MLOps expertise, advanced Python/SQL skills, and domain specialization; enforce code quality standards across the team Own key features and projects; design and improve models, pipelines, and experimentation frameworks Lead 1+ major projects as AI l