• Insight that drives product decisions. • You identify the highest-leverage questions, build the frameworks to answer them, and bring clear recommendations to product and leadership. PMs and executives make different decisions because you're in the room. • Player-coach leadership. • You manage and develop a team of product analysts across our highest-priority charters. You take the hardest, highest-leverage work yourself — and make everyone around you better at theirs through hiring, coaching, and the standards you set. • Accountable for growth outcomes. • You're measured on the business impact of the decisions analytics influences — conversion, retention, revenue per lead — not on the volume of analyses produced. You operate across product areas, experiments, and data sources, surfacing patterns and strategic bets that siloed analysis misses, and you shift how leadership thinks about growth. • Deep customer and business awareness. • You build a rich model of how customers behave, what drives conversion and retention, and where growth opportunities live. That model shapes everything from long-term roadmap bets to the micro-decisions made in weekly product reviews. • Experimentation at portfolio scale. • Own experiment strategy across your product areas — design, guardrails, interpretation, and the cross-experiment patterns that reveal the bigger picture no single test shows. • AI system quality and judgment. • Our agents pull data, read configs, and produce draft analyses. You own the quality of that output across the team — validating it, sharpening it, and training the system over time. Human judgment over AI judgment. Always. • Raising the bar for the entire team. • You set the standard for how AI and analytical rigor work collaboratively. When you find a better way to validate an experiment, pressure-test a metric, or get signal from AI faster — you make it the team's new baseline, and you make sure the team can execute it without you.

• Atuar em colaboração com os times de Crédito, incluindo as áreas de Política, Análise, Monitoramento e Cobrança. • Conduzir projetos de Data Science de ponta a ponta, desde a definição e recorte do problema com Produto/Operações, criação da solução, experimentação, até a entrega em produção • Realizar rotinas de acompanhamento de data drift e concept drift, utilizando frameworks como NannyML. • Realizar estudos, identificar padrões e insights que colaborem para a eficiência da nossa operação de Crédito. • Desenvolver e realizar a manutenção de métricas, KPIs, relatórios e dashboards sobre os nossos produtos e operação de Crédito. • Manter-se atualizado(a) em melhores práticas de Data Science aplicada e produtividade em desenvolvimento (incluindo ferramentas de desenvolvimento assistido por IA), avaliando e propondo melhorias que façam sentido para o contexto e qualidade de entrega.

Role Description We are looking for a Senior Data Scientist to develop and deploy advanced analytics and AI solutions that drive business impact in the Consumer Insights and Analytics team. As a Strategic Partner to Marketing and Sales teams, success in this role requires a strong analytical mindset, expertise in building machine learning models, optimizing performance, and integrating cutting-edge technologies to enhance decision-making and automation. Key Responsibilities - Advanced Analytics & Insights: Develop and implement data science solutions, leveraging advanced analytics to drive data-driven decision-making, optimize business performance, and support Consumer Insights & Analytics priorities. - Data Analysis & Modeling: Analyze large and complex datasets to identify patterns, build statistical and machine learning models, and solve critical business problems using big data technologies like Databricks, Azure ML, and Apache Spark, as well as strong data visualization for structured storytelling to drive impactful business decisions. - Developing Advanced Models: Design deep learning and natural language processing models to analyze complex data across business processes. - Model Monitoring & Performance Optimization: Continuously track, evaluate, and refine deployed models to maintain accuracy and relevance over time. - AI Research & Evaluation: Research, test, and evaluate AI technologies to ensure the best tools and platforms are used for achieving business objectives. - Enhancing Analytical Capabilities: Integrate the latest data science innovations into product solutions, enhancing data, analytical, and automation capabilities. - Cross-Functional Collaboration: Work closely with cross-functional teams and stakeholders to provide expertise across omnichannel analytical projects and ensure alignment with business goals. - Tool Development & Maintenance: Build, maintain, and improve analytics tools to enhance capabilities and accelerate speed to value. Qualifications - Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Mathematics, Physics, Operations Research, Statistics, or a related field. - 5+ years of experience as a Data Scientist, Machine Learning Engineer, or Applied Scientist, specializing in computer vision, natural language processing, or data synthesis. - Experience developing and deploying advanced analytical models and algorithms. - Experience applying machine learning techniques, including Regression, Clustering, Probability Networks, Artificial Intelligence (AI), and Deep Learning. - Hands-on programming experience in Python and R, leveraging advanced cognitive and machine learning methods to solve business challenges. - Experience working with relational database and cloud computing technologies such as Databricks, Snowflake, AWS, Azure ML and AMC, Big Query. - Experience in transforming raw data into meaningful features for machine learning models. - Expertise in handling missing data, scaling, encoding categorical variables, and dimensionality reduction techniques. - Hands-on experience in designing and analyzing A/B tests and other experimental methodologies. - Hands-on experience in causal inference techniques to measure the impact of business decisions. Skills - Experience visualizing and presenting data-driven insights using Power BI and Tableau. - Experience in developing recommendation engines for applications such as sales, customer targeting, or supply chain optimization. - Experience in building and implementing optimization models to enhance decision-making processes. - Familiarity with agile methodologies and rapid prototyping in data science projects. - Strong knowledge of industry-standard tools and methodologies for model training, statistical analysis, and machine learning deployment. - Experience working with CPG and ecommerce data sets, including retail analytics and advertising data, is a plus. - Candidates that have experience working with AMC (Amazon Marketing Cloud) and Amazon advertising and sales data will be given priority consideration. Compensation and Benefits - The approximate pay range for this position is $130,000 to $160,000. - Nestlé offers performance-based incentives and a competitive total rewards package, which includes a 401k with company match, healthcare coverage, and a broad range of other benefits. Inclusivity Statement It is our business imperative to remain a very inclusive workplace. To our veterans and separated service members, you're at the forefront of our minds as we recruit top talent to join Nestlé. The skills you've gained while serving our country, such as flexibility, agility, and leadership, are much like the skills that will make you successful in this role.

• Define how marketing and customer data is captured, structured, modeled, and reported across the organization • Own the end-to-end marketing measurement strategy across paid media, SEO, lifecycle marketing, affiliate, social, website, and revenue analytics • Define and maintain attribution models including first-touch, last-touch, multi-touch, and blended attribution • Establish source-of-truth logic for key business metrics including: Customer Acquisition Cost (CAC), Return on Ad Spend (ROAS), Customer Lifetime Value (LTV), Funnel conversion rates, Retention metrics, Payback period, Channel performance and contribution • Identify gaps in tracking, reporting, attribution, and data quality • Partner with leadership to prioritize analytics initiatives based on business impact • Define analytics requirements across the marketing data lifecycle • Collaborate with data engineers and developers on: • Event tracking architecture • Attribution persistence • Identity resolution and customer stitching • Data ingestion pipelines • Reporting-ready data models • Revenue reconciliation • Validate datasets and reporting logic within BigQuery or similar cloud data warehouse environments • Ensure marketing and product data can be accurately joined across systems • Own event taxonomy and tracking requirements across web, lifecycle, checkout, and product experiences • Partner with developers to ensure accurate implementation of: • Google Analytics 4 (GA4) • Google Tag Manager (GTM) • Meta Pixel and Conversions API (CAPI) • Google Ads tracking • Hyros and attribution platforms • Intercom and customer engagement tools • Maintain UTM governance and campaign naming conventions • Ensure click IDs and attribution parameters are captured and persisted correctly • Build or oversee dashboards and reporting systems using: • Tableau • Looker Studio • Power BI • Similar BI and analytics platforms • Create reporting frameworks and executive KPI dashboards • Translate complex analytics findings into clear business insights and recommendations • Support weekly and monthly business reviews with actionable reporting narratives • Create QA processes for analytics implementation and reporting accuracy • Monitor tracking integrity and proactively identify anomalies or reporting discrepancies • Maintain analytics documentation and reporting standards • Ensure stakeholders understand limitations or directional nature of data when applicable