{"id":797469,"url":"https://alion.io/job/vonage-marketing-data-scientist-2","title":"Marketing Data Scientist","company":{"id":58890,"name":"Vonage","domain":"vonage.com","url":"https://alion.io/company/vonage","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":93,"open_postings":7,"ghost_share":0,"stale_share":0.286,"repost_share":0,"time_to_fill_p50_days":56,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":34000,"max_usd":89000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2491},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitLab","optional":false},{"name":"Google Sheets","optional":false},{"name":"LLM","optional":false},{"name":"NumPy","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"Tableau","optional":false},{"name":"WhatsApp","optional":true}],"status":"live","first_seen_at":"2026-05-13T15:26:10Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-20T17:32:23Z","board_verified":true,"closed_at":null,"days_open":160,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":159},"description":"Join Vonage and help us innovate cloud communications for businesses worldwide!\nWhy this role matters: \nWe are looking for a Data Scientist to own our demand and revenue intelligence capabilities. You will sit at the intersection of marketing strategy and commercial outcomes, building the predictive models and analytical frameworks that tell us where growth is coming from before it arrives. Your work will directly shape how we reach the right audiences, invest in the right channels, and accelerate pipeline - turning marketing activity into a measurable, forward-looking revenue engine. \nYour key responsibilities: \nAnalyze and model the relationship between marketing investment, channel performance, and revenue outcomes, connecting spend to growth \nDesign and maintain reporting views and self-serve analytics assets for internal stakeholders and marketing partners; making model outputs and performance data accessible, consistent, and actionable across teams \nIdentify high-value market segments and audiences through data modeling to inform how and where we compete \nDevelop personalization models and frameworks that enable tailored customer experiences across channels, from dynamic content and offers to next-best-action recommendations \nCollaborate with Marketing and Sales teams to define forecasting methodologies and embed models into operational workflows \nTranslate complex model outputs into clear, actionable narratives that drive strategic decisions at the executive level \nContribute to the development of AI-ready data structures in Snowflake, including clean feature tables, scoring outputs, and audience segments that serve as reliable inputs for marketing automation and AI-driven decisioning \nWhat you'll bring: \nStrong understanding of the full B2B revenue funnel - from awareness and demand generation through pipeline to closed revenue - and how data flows across it \nStrong business acumen with the ability to connect predictive model outputs to strategic marketing and revenue decisions \nThe ability to influence RevOps, Sales, and Marketing stakeholders through data-led storytelling \nA rigorous yet pragmatic approach to forecasting - knowing when to build a sophisticated model and when a well-structured regression is enough \nA proven self-starter comfortable operating in fast-moving, commercially-driven environments \nA working fluency with AI tools and LLM-based workflows - able to leverage AI to accelerate analysis, prototype models, and operate effectively in an AI-first data environment \nRequired: \n8 years of experience in Data Science, Revenue Analytics, or a related field \nSalesforce data experience: proven hands-on experience with Salesforce data across the full object model (Leads, Contacts, Accounts, Opportunities, Campaigns), and a deep understanding of how marketing and sales data interconnects within Salesforce \nSnowflake data warehouse experience: demonstrated experience querying, modeling, and working with data in Snowflake, including SQL-based data modeling and working with structured analytical data sets \nExperience modeling pipeline generation, conversion rates, and revenue velocity across the full funnel \nTools & Technologies: \nSalesforce - full object model (Leads, Contacts, Accounts, Opportunities, Campaigns), SOQL, Salesforce administration, Salesforce Einstein Analytics / Tableau CRM \nPython - scikit-learn, statsmodels, Prophet, pandas, NumPy for forecasting and modeling \nSQL - advanced querying across CRM and marketing data sources \nData Visualization - Tableau, Power BI, Salesforce-native dashboarding \nMarketing Automation Platforms - Marketo, HubSpot, or Pardot (data structure familiarity) \nCloud Data Platforms - Snowflake, AWS, GCP, or Azure for data extraction and model deployment \nVersion Control - Git/GitHub or GitLab \nSpreadsheet & Presentation Tools - Excel, Google Sheets for stakeholder-facing outputs \nWhat we consider a plus: \nExperience with customer lifetime value (LTV) modeling and churn prediction \nFamiliarity with marketing automation platforms and how they feed into Salesforce (e.g., Marketo, HubSpot) \nExperience with lead-to-revenue funnel analytics in a B2B SaaS environment \nWorking knowledge of digital marketing platforms and their underlying data structures \nAdvanced degree (Master's or PhD) in a quantitative field such as Statistics, Economics, or Computer Science \n#LI-JS3\nDisclaimer: The posted range represents the good faith salary for this role at the time of posting. Final compensation is determined by factors including (but not limited to) geographic location, relevant experience, specific skill sets, and internal equity.\nThere’s no perfect candidate. You don't need all the preferred qualifications to make a valuable impact on our team. Our employees and customers come from diverse backgrounds, so if you're passionate about what you could achieve at Vonage, we'd love to hear from you.\nTo learn how we process your personal data during the recruitment process, please refer to our Privacy Notice. \nWho we are:\nVonage is a global cloud communications leader. And your talent will further help brands - such as Airbnb, Viber, WhatsApp, and Snapchat - accelerate their digital transformation through our fully programmable-based unified communications, contact center solutions, and communications APIs. Ready to innovate? Then join us today.\nNote: The purpose of this profile is to provide a general summary of essential responsibilities for the position and is not meant as an exhaustive list. 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