E

Experian

Venture capital arm funding data startups

Senior Data Analyst - CRM PME

Full-Time
No salary listed
Senior
São Paulo, State of São Paulo, Brazil
Remote
Data & AnalyticsData Analysis

About the job

Requirements
  • Solid experience in data analysis, CRM analytics, marketing analytics, customer intelligence, lifecycle analytics, or related areas.
  • Advanced SQL skills for data extraction, processing, analytical modeling, and joining large datasets.
  • Advanced knowledge of Excel or Google Sheets.
  • Experience with business intelligence and data visualization tools such as Power BI, Tableau, Looker, or similar tools.
  • Experience analyzing lifecycle, cohorts, funnels, segmentation, clusters, behavior, propensity, and campaign performance.
  • Consistent knowledge of CRM and digital marketing metrics, including retention, churn, lifetime value, conversion, engagement, usage frequency, product penetration, and incrementality.
  • Familiarity with Next Best Offer and Next Best Action applications, propensity models, offer recommendations, audience prioritization, and journey personalization.
  • Experience translating analytical outputs or predictive models into actionable communication, segmentation, and relationship strategies.
  • Experience with A/B testing, control groups, impact measurement, basic statistical interpretation, and experimentation methodologies.
  • Knowledge of CRM, marketing automation, or MarTech platforms such as Salesforce, Adobe Campaign, Braze, Oracle, or RD Station.
  • Advanced analytical thinking and the ability to structure complex problems.
  • Strategic business perspective and the ability to connect data, customers, products, channels, and financial outcomes.
  • Ability to bridge technical and business teams.
  • Communication skills to translate analyses, models, and recommendations into clear actions for CRM teams and stakeholders.
  • Investigative curiosity, critical thinking, and a consultative approach.
  • Proactivity in identifying opportunities, risks, inconsistencies, and paths for improvement.
  • Ability to influence decisions using evidence, tests, and results.
  • Organization, autonomy, and responsibility for high-impact business deliverables.
  • Ability to collaborate across CRM, Marketing, Commercial, Product, Data Science, Analytics, and Technology teams.
  • Results orientation, with a focus on impact, continuous learning, and performance improvement.
Responsibilities
  • Support the evolution of the SME CRM strategy using data, customer behavior, lifecycle, campaign performance, and business opportunities.
  • Connect customer analyses to acquisition, activation, engagement, retention, reactivation, cross-sell, upsell, customer protection, and churn-reduction strategies.
  • Identify opportunities to personalize communications, offers, and journeys based on customer profile, segment, channel, contracted product, behavior, maturity, and relationship stage.
  • Translate complex analyses into practical recommendations for CRM, Marketing, Commercial, Product, and other stakeholders.
  • Support the definition of audiences, journeys, hypotheses, messages, channels, and contact timing based on evidence and data.
  • Contribute to improving CRM indicators, including engagement, conversion, revenue, retention, churn, reactivation, product penetration, lifetime value, customer value, and campaign efficiency.
  • Act as an analytical reference for SME CRM decisions, improving the quality of hypotheses, result interpretation, and the team's data-driven culture.
  • Bridge the SME CRM team and the Data Science team responsible for Next Best Offer and Next Best Action models.
  • Interpret model outputs and translate them into actionable opportunities for campaigns, journeys, communication sequences, digital channels, and commercial actions.
  • Support hypotheses for using models in communications, accounting for audience, offer, recommended action, channel, message, frequency, and contact timing.
  • Propose A/B tests, incrementality tests, pilots, and controlled experiments to evaluate the effectiveness of Next Best Offer and Next Best Action in CRM activities.
  • Build analyses that support adoption of intelligence in communications by demonstrating gains, learnings, limitations, and opportunities for improvement.
  • Monitor the performance of actions using Next Best Offer and Next Best Action, assessing engagement, conversion, revenue, offer acceptance, product usage, retention, cross-sell, upsell, and incremental impact.
  • Identify response patterns by segment, product, channel, journey stage, and recommendation type, generating learnings for ongoing optimization.
  • Support prioritization of audiences and recommendations by connecting the model's analytical rationale to actual SME CRM and business needs.
  • Work with Data Science to feed models with CRM learnings, campaign results, response behavior, and opportunities observed in operations.
  • Ensure that Next Best Offer and Next Best Action are applied practically, measurably, and in alignment with SME customer-relationship strategy.
  • Lead analyses of customer bases, cohorts, funnels, journeys, channels, products, clusters, behavior, propensity, and campaign performance.
  • Build and improve dashboards, executive reports, and recurring analytical views for SME CRM monitoring.
  • Support the development of analytical datasets, segmentation, eligibility rules, prioritization criteria, and opportunity studies.
  • Ensure clarity, reliability, and actionability of the indicators used in CRM decisions.
  • Use multiple data sources to build a more integrated customer view, considering relationships, consumption, engagement, commercial history, channel interactions, and contracted products.
  • Identify data inconsistencies, gaps, automation opportunities, and improvements to CRM analytical processes.
  • Help reduce overlapping communications, improve contact pressure, and increase interaction relevance.
  • Support the development of automated journeys that dynamically respond to customer behavior.
  • Contribute to greater operational efficiency in campaigns, segmentation, and performance analyses.
  • Define and conduct performance analyses to assess the impact of campaigns, journeys, communication sequences, offers, commercial actions, and intelligence-led communications.
  • Structure A/B and multivariate tests, control groups, and incrementality analyses when applicable.
  • Evaluate business hypotheses rigorously, connecting results to clear learnings and optimization recommendations.
  • Measure the impact of CRM actions on conversion, revenue, engagement, product usage, retention, churn, cross-sell, upsell, and lifetime value.
  • Create analytical narratives that help diverse audiences, from technical teams to business leaders, make decisions.
  • Turn complex data into diagnoses, recommendations, and action plans.
  • Document learnings about what works, for which audiences, in which channels and journey stages, and with which types of recommendations.
  • Support a continuous-learning routine linking hypotheses, tests, results, and next steps.
Desired Qualifications
  • Knowledge of Python, R, or other languages for data analysis.
  • Experience with customer data platforms, CRM datamarts, data-channel integration, predictive models, or recommendation engines.
  • Previous experience with B2B CRM, small and medium-sized businesses, financial services, credit, collections, data, marketing services, or digital products.
  • Experience with Next Best Offer or Next Best Action strategies, offer recommendations, advanced personalization, or lifecycle marketing.
  • Experience activating analytical models in campaigns, journeys, digital channels, or commercial actions.
  • Experience with Salesforce, customer data platforms, automation tools, data platforms, or integration between analytical models and activation channels.
  • Knowledge of portfolio analysis, product penetration, portfolio expansion, cross-sell, upsell, and customer-base monetization.
  • Experience in environments focused on revenue, conversion, retention, commercial efficiency, and campaign performance goals.

About the company

Experian Ventures is the venture capital arm of Experian, a global information services company. It provides investment capital and strategic partnerships to startups, helping them grow by leveraging Experian's data assets, technology, and global network. The fund focuses on backing entrepreneurs who can disrupt data- and tech-driven markets, using Experian's platforms and reach to accelerate portfolio companies. Unlike standalone funds, it differentiates itself with access to Experian's scale, data resources, and worldwide offices, enabling closer collaboration and faster go-to-market support. The goal is to fund and mentor startups that can shape the future of information services while expanding Experian's ecosystem and influence across regions.

Company Size

1-10

Company Stage

N/A

Total Funding

$82M

Headquarters

Costa Mesa, California

Founded

1995

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Simplify's Take

What believers are saying

  • FY26 revenue reached $8.45 billion, with 8% organic growth and 15% benchmark EPS growth.
  • Experian announced a new $1 billion buyback on May 20, 2026, after $725 million repurchases.
  • KYC360, AtData, ClearSale, and Own Up expanded fraud, identity, and mortgage distribution.

What critics are saying

  • CFPB sued Experian on January 7, 2025 over credit dispute resolution practices.
  • May 20, 2026 guidance of 6-8% organic growth signaled slower lending demand and AI anxiety.
  • Banks and AI risk tools threaten credit bureau disintermediation if lenders internalize underwriting by 2027.

What makes Experian unique

  • Experian controls proprietary data on 250 million Americans and 10 billion emails after AtData.
  • Experian launched VantageScore 4.0 across mortgages and rentals, including Zillow on March 31, 2026.
  • Experian Agent Trust and Ascend embed AI, fraud, and compliance into client workflows.

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Benefits

  • Remote Work Options
  • Professional Development Budget

Company News

Yahoo Finance
May 20th, 2026
Experian's $1B buyback fails to halt 7% stock plunge despite $1.9B profit surge

Experian reported a 26% increase in statutory pre-tax profit to $1.95 billion and announced a $1 billion share buyback programme, yet its stock plunged 7% as investors focused on conservative growth forecasts and AI concerns. The data analytics company posted full-year revenue of $8.45 billion with 15% growth in benchmark operating profit. However, its fiscal 2027 organic revenue growth guidance of 6-8% fell below the 8% analyst consensus. CEO Brian Cassin cited economic volatility and cautious banking clients slowing credit card issuance. The sell-off reflects investor anxiety that generative AI could make traditional credit checking obsolete. Markets interpreted the massive buyback as defensive rather than transformational, questioning whether Experian can compete as banks develop internal AI tools for risk analysis.

Yahoo Finance
May 12th, 2026
Experian launches AI chatbot on Snapchat to give financial advice to younger users

Experian has launched an AI chatbot on Snapchat to provide financial advice, targeting younger consumers on a platform they already use. The credit bureau aims to deliver trusted financial information to help build financial literacy amongst users who might not otherwise seek professional guidance. Rob Burnette, CEO of Outlook Financial Center, views AI chatbots as useful tools for answering broad financial questions, such as improving credit scores. However, he cautions that AI cannot address individual circumstances without specific personal data, which users should avoid sharing online. Burnette recommends verifying information sources when using AI chatbots and consulting human advisers for personalised financial decisions. Whilst not threatened by AI, he emphasises its limitations in handling the nuanced, context-specific nature of personal finance.

PR Newswire
May 12th, 2026
Xactus embeds free employment preview report into credit checks for mortgage lenders

Xactus has integrated the Experian Verify Preview Report into its Xactus360 Intelligent Verification Platform, providing lenders with upfront visibility into borrower employment data at no additional cost. The tool allows lenders to see which employment records are available for instant verification before ordering a full verification of employment and income report. Previously, lenders had to order a full VOE/I report to determine if usable data existed. Xactus is the first to embed this capability directly within the credit report workflow, eliminating the need for separate verification relationships or additional steps. The enhancement is automatically included for Xactus clients who order credit reports through the platform and use Experian Verify, requiring no new integrations or workflow changes.

Techla
May 6th, 2026
Palenca raises $4M to make millions of Mexicans' income visible for lenders

Palenca, a Mexican income verification platform for financial institutions, has raised $4 million in a Series A round led by Experian, with participation from Foundation Capital, Gilgamesh Ventures and Dhow Ventures. The company addresses a critical gap in Mexico's financial inclusion: the lack of reliable income data prevents credit expansion to new segments. Without verifiable income information, financial institutions must either limit their reach or assume greater risk. Palenca's platform validates income from various sources, including informal employment, to integrate them into credit and risk decisions. The funding will develop new data products, expand income source validation and strengthen predictive models using artificial intelligence. The company processed nearly 5 million verifications in 2025 and projects reaching 20 million this year, enabling banks to extend credit to previously excluded populations.

Yahoo Finance
May 4th, 2026
Snap integrates Experian's AI financial tools into new Sponsored Snaps ad format

Experian has integrated its AI-powered financial education tools into Snapchat's new AI Sponsored Snaps advertising format, allowing users to ask money questions directly within the app's chat experience. The integration aims to transform everyday messaging into an interactive learning and brand engagement channel for younger users. Snap's investment case hinges on whether its combination of augmented reality, AI and Gen Z engagement can generate profitable revenue. The company reported over 110 million US monthly users and 950 billion chats in Q1 2026. The Experian partnership demonstrates how Snap is testing conversational AI to deepen engagement and improve advertising performance. Snap's narrative projects $7.9 billion revenue and $709.3 million earnings by 2029, requiring 10.2% yearly revenue growth. Some analysts forecast $8.5 billion revenue by 2029, though this depends on AR and AI investments delivering stronger returns than currently expected.