• Definir visão, arquitetura e estratégia técnica para IA generativa; • Desenvolver soluções com agentes autônomos, copilotos inteligentes e assistentes personalizados; • Atuar como referência técnica para o time de ciência de dados, apoiando no desenvolvimento técnico e adoção de boas práticas; • Propor e implementar padrões de engenharia de software e ciência de dados, como versionamento, reprodutibilidade e documentação; • Colaborar com times de engenharia, produto e dados para garantir integrações eficientes e uso sustentável da infraestrutura de dados.

• Desenvolver soluções técnicas que atendam os requisitos de negócio, buscando a criação de aplicações escaláveis e performáticas; • Desenvolver sistemas batch e APIs Rest em linguagem Python, baseado na documentação fornecida, criando aplicação que atendam às exigências do usuário; • Realizar manutenção em portais web; • Identificar, corrigir incidentes e realizar manutenção em sistemas, de acordo com a descrição do problema e a análise de logs; • Elaboração de documentação técnica da solução implementada; • Elaboração de testes unitários; • Capacidade de interação com área de negócios com objetivo de elucidar requisitos de negócios, homologar sistemas desenvolvidos e contribuir com informações técnicas em reuniões de alinhamento da demanda; • Desejável ter atuado com metodologias ágeis; • Desenvolver e avaliar soluções baseados em IA generativa (LLMs) aplicadas a problemas reais de negócio, como recomendação de produtos, análise de documentos, chatbot e estratégias de cobrança; • Conduzir experimentos para otimização de modelos, incluindo prompt engineering, fine-tunning e técnicas de RAG (Retrieval -Augmented Generation); • Definir e implementar métricas de avaliação para modelos generativos (ex: qualidade, factualidade, consistência e viés); • Realizar análises estatísticas robustas para validação de resultados, incluindo testes de hipótese, intervalos de confiança e simulações; • Identificar riscos e propor soluções para mitigação de alucinações, vieses e problemas de confiabilidade.

WHO WE ARE Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com. The Role Zeta Global is looking for a motivated and curious Data Scientist to join our Machine Learning team in Copenhagen, working within the Agentic DSP pod. Our Copenhagen office is one of the strongest engineering hubs in the company, bringing together a highly skilled group of engineers, data scientists, and researchers with backgrounds in Physics, Mathematics, and Computer Science. The environment combines deep technical expertise with a pragmatic, product-focused mindset. You will collaborate closely with our global teams across San Francisco, New York, Berlin, and Prague, contributing to the development of next-generation machine learning systems powering our AI-driven advertising platform. As a Data Scientist, you will work alongside experienced engineers and scientists to design, analyze, and improve machine learning models used in a large-scale, real-time system. This is an entry-level role, and we do not require prior industry experience. Instead, we are looking for strong fundamentals, intellectual curiosity, and a willingness to learn. Key Responsibilities: - Analyze large datasets to extract insights and support model development - Contribute to the design and evaluation of machine learning models - Work closely with software engineers to bring models into production - Participate in experimentation, prototyping, and iterative improvement - Learn how real-world ML systems are built, deployed, and maintained at scale - Explore and apply modern ML/AI techniques, including deep learning and AI agents, to develop and improve cutting-edge probability prediction models in a real-time DSP environment Your Profile We are looking for candidates with strong analytical foundations and a passion for solving complex problems. Minimum Qualifications: - Strong academic background in Physics, Mathematics, Computer Science, or a related quantitative field - Solid understanding of mathematics, statistics, or algorithms - Basic knowledge of machine learning and AI - Experience with software engineering principles (through studies, projects, or internships) - Experience programming in Python - Curiosity, self-motivation, and eagerness to learn - Ability to translate real-world problems into structured, analytical solutions - A MSc in a relevant field Nice to Have: - Experience programming in Scala - Experience working with very large datasets (projects, coursework, or research) - Familiarity with tools such as Spark or distributed systems - A PhD in a relevant field What we offer - A chance to join a top-tier engineering team in Copenhagen with a strong academic and technical foundation - Close collaboration with global teams across the US and Europe - A learning-focused environment where junior talent is mentored and developed - Exposure to real-world, large-scale machine learning systems - A culture of autonomy, ownership, and collaboration - Flexible working conditions and modern office environment Benefits and Perks: - Excellent medical, and vision coverage Why join us? This is a unique opportunity to start your career in data science by working on complex, high-impact systems alongside experienced professionals, in an environment that values both deep technical thinking and practical execution. PEOPLE & CULTURE AT ZETA Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression. We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/ ZETA IN THE NEWS! https://zetaglobal.com/press/?cat=press-releases #LI-NP1

Title: Principal Data Scientist Locations: Florida, Texas, Illinois, New York, New Jersey, Colorado, Idaho, Massachusetts, Michigan, Minnesota, Missouri, North Carolina, South Carolina, Utah or Virginia Remote Job Description: #WeAreTradeStation Remote Position - must reside in Florida, Texas, Illinois, New York, New Jersey, Colorado, Idaho, Massachusetts, Michigan, Minnesota, Missouri, North Carolina, South Carolina, Utah or Virginia Who We Are: TradeStation is the home of those born to trade. As an online brokerage firm and trading ecosystem, we are focused on delivering the ultimate trading experience for active traders and institutions. We continuously push the boundaries of what's possible, encourage out-of-the-box thinking, and relentlessly search for like-minded innovators. At TradeStation, we are building an AI-First culture. We expect team members to embrace AI as a core part of their daily workflow, whether that’s using AI to accelerate development, enhance decision-making, improve client outcomes, or streamline internal processes. We hire, grow, and promote people who can harness AI responsibly and creatively. We treat AI as a partner in problem-solving, not just a tool; following our governance standards to ensure AI is used ethically, securely, and transparently. If you join us, you’re joining a culture where AI is how we work. Are you ready to make yourself at home? What We Are Looking For: We're seeking a Principal Data Scientist to help design, build, and deploy advanced analytics and machine learning solutions that power TradeStation's trading platform, client experience, and business operations. Reporting to the Sr. Director of AI, Data Science & Enterprise Data, this role will own the full data science lifecycle — from exploratory analysis and model development through production deployment and ongoing monitoring. This role is for a self-directed practitioner who thrives without hand holding. The ideal applicant will proactively identify opportunities where data science can create meaningful impact, build the solutions, and iterate rapidly. The Principal Data Scientist will write clean, production-grade code, stay genuinely curious about emerging AI and ML techniques, and independently learn new tools before being asked to. The applicant should be equally at home in a Jupyter notebook and a production ML pipeline. This role will work closely with Product, Engineering, Compliance, and Analytics teams to build predictive models, behavioral analytics, and AI-powered capabilities that give TradeStation a competitive edge. What You’ll Be Doing: Modeling & Machine Learning - Own the end-to-end ML lifecycle — from problem framing and feature engineering through model training, validation, deployment, and ongoing performance monitoring - Help build and deploy predictive models across a range of use cases including customer behavior, fraud and anomaly detection, trade surveillance, risk modeling, and personalization - Design and implement real-time and batch ML pipelines that operate reliably at scale in production environments - Develop behavioral anomaly detection and pattern recognition systems using statistical and deep learning approaches - Apply NLP and LLM techniques to extract insights from unstructured data — trade notes, client communications, market commentary, and internal documentation Analytics & Visualization - Translate complex data into clear, compelling visualizations and narratives for both technical and executive audiences - Help design and build dashboards and analytical tools that empower stakeholders to make faster, more informed decisions - Conduct exploratory data analysis to surface trends, anomalies, and opportunities across trading behavior, customer segments, and platform performance - Define, track, and interpret key business and model performance metrics; proactively surface meaningful insights without waiting to be asked AI Integration & Innovation - Stay at the forefront of AI and ML research — continuously evaluate and adopt emerging techniques (GenAI, RAG, agents, multimodal models) where they create real business value - Leverage AI tools (Claude, LLMs, foundation models) to accelerate your own development workflow, from code generation to documentation to data profiling - Experiment rapidly with new approaches; fail fast, iterate, and bring winning solutions to production - Contribute to TradeStation's AI governance standards by ensuring models are interpretable, fair, and deployed responsibly Strategic Impact & Communication - Partner with Product and Engineering to define the data and modeling requirements for new platform features - Work with Compliance and Risk teams to build surveillance and monitoring systems that meet regulatory requirements - Communicate results and recommendations clearly to non-technical stakeholders; translate business questions into rigorous analytical frameworks The Skills You Bring: - Self-Starter & Independent Learner —proactively identify problems worth solving, learn new techniques without being prompted, and drive projects to completion without needing direction - Full-Stack Data Science — proven ability to own the complete lifecycle: problem definition, data wrangling, feature engineering, modeling, deployment, and monitoring in production environments - Machine Learning Depth — strong command of supervised, unsupervised, and reinforcement learning methods; experience with time series, anomaly detection, NLP, and deep learning; know when to use simple models and when to go complex - Software Engineering Fundamentals — writes production-quality Python; comfortable with version control (Git), containerization (Docker), and MLOps best practices; code that others can maintain - Data Platform Proficiency — hands-on experience with Databricks, Spark, or Snowflake; able to write and optimize complex SQL; understands data modeling and pipeline design - Visualization & Storytelling — ability to build polished, insight-driven visualizations and dashboards (Tableau, Power BI, Plotly, Sigma); presents data science work in business terms - AI-Native Workflow — actively uses AI tools (Claude, Copilot, LLMs) in day-to-day work; has hands-on experience with LLM APIs, prompt engineering, or GenAI application development - Statistical Rigor — solid grounding in probability, statistics, and experimental design; applies A/B testing and causal inference correctly; doesn't overfit spurious signals - Cross-Functional Collaboration — comfortable working across Product, Engineering, Compliance, and Analytics; can present findings to executives and translate business requirements into analytical solutions - Financial Services Domain— experience with trading data, market microstructure, customer behavior in financial platforms, fraud detection, or regulatory compliance analytics strongly preferred - Experience building and monitoring ML models in production using MLflow, SageMaker, Vertex AI, or similar MLOps platforms preferred - Hands-on experience with LLM APIs, RAG architectures, or AI agent frameworks preferred - Track record of self-directed learning — personal projects, open-source contributions, Kaggle competition history, technical writing, or conference presentations preferred - Experience with fraud detection, behavioral anomaly detection, trade surveillance, or risk modeling in financial services preferred - Familiarity with real-time streaming data (Kafka, Spark Streaming) and low-latency model serving preferred - Experience with cloud ML infrastructure (Azure, AWS, or GCP) and distributed computing preferred Minimum Qualifications: - Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field - 7+ years of experience in data science or applied machine learning roles, with demonstrated ownership of models deployed to production Desired Qualifications: - Master's or PhD in a quantitative discipline What We Offer: - Collaborative work environment - Competitive Salaries - Yearly bonus - Comprehensive benefits for you and your family starting Day 1 - Unlimited Paid Time Off - Flexible working environment - TradeStation Account employee benefits, as well as full access to trading education materials - Pay Range (US) $140-180K (Countries outside of the US have differing ranges in accordance with local labor markets) TradeStation provides equal employment opportunities to current and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, sexual orientation, age, pregnancy, disability, handicap, citizenship, veteran or marital status, or any other legally recognized status entitled to protection under federal, state, or local anti-discrimination laws. #LI-Remote