• Build and Deploy Analytical Models • Design, develop, and validate predictive models — forecasting, classification, regression, and segmentation — with rigorous diagnostics and clear documentation • Conduct A/B testing and causal analyses to support experimentation programs and measure the impact of client initiatives • Develop optimization solutions (linear, mixed-integer, multi-objective) and ensure models are production-ready with monitoring in place • Use Databricks for large-scale data processing and machine learning workflows, integrating data from diverse sources across complex client environments • Monitor and validate data flows, develop data quality reporting, and perform root-cause analysis when issues surface • Collaborate with data engineering teams on MLOps practices — MLflow pipelines, model deployment, lifecycle management, and version control via Git/GitLab • Execute advanced analyses, synthesize findings, and build executive-ready presentations and visualizations using Tableau and Power BI • Develop new metrics and KPI reports that give clients and internal stakeholders a clearer view of business performance • Communicate effectively with both business and technical audiences — adapting your framing to the room • Work with business partners and data engineering teams to elicit requirements, define business rules, and translate client needs into technical specifications • Contribute to project meetings to align on scope, timelines, and analytical approach across multi-member engagements • Test database changes prior to release and ensure data designs align with defined standards and governance requirements • Apply data governance, privacy, and compliance standards consistently across analytical work • Document models, processes, and business rules clearly enough that others can reproduce, validate, and build on your work

• Leverage education and experience in data visualization to support data-driven storytelling initiatives across the tower focusing on GM as a first priority. • Leverage Acxiom’s data and data available through established partnerships with brands like Equifax, Polk and Commerce Signals to develop opportunities for creating high-value audiences and other models. • Help coordinate and prepare for activities designed to build, test and perfect data solutions

About the Role Liquid Web is hiring a Data Scientist to power our volumetric, subscription-driven growth engine (hosting, commerce, and software). This role owns the models and measurement systems connecting marketing spend to conversion, retention, and LTV, with a critical emphasis on ICP-specific economics (agencies, SaaS, ecommerce, and mission-critical buyers). You aren’t just a "dashboard builder." You are a Data Anthropologist. You look past the rows in Snowflake to see the human stories—the friction points that cause churn, the "aha" moments that trigger an upgrade from VPS to Dedicated, and the behavioral patterns that separate a hobbyist from a mission-critical enterprise buyer. You’ll build and operationalize LTV and churn models, develop marketing attribution approaches, and partner across Marketing, Product, RevOps, and Finance. You will also help hire and lead a reporting team, setting the standard for metrics, dashboards, and governance in Power BI. This Job is For You If: - You speak "Finance" and "Creative" fluently. You can explain $LTV$ to a CFO and conversion attribution to a Copywriter without losing the soul of either. - You are a "Pattern Hunter." You don't just report that churn happened; you find the "Silent Churn" indicators three months before the customer even thinks about canceling. - You loathe "Vanity Metrics." You find no joy in "Total Page Views." You live for Contribution Margin and Cohort Payback Periods. - You’re a Power BI Poet. Your DAX is elegant, your star schemas are pristine, and your dashboards tell a story so clear that an executive can understand the "Why" in under ten seconds. What You’ll Do - Subscription Economics & LTV Modeling: Build predictive LTV models accounting for upgrades, churn, and margin. Develop payback and CAC views to support budget allocation and align with Finance on contribution LTV and time horizons. - Expansion Strategy (Upsell & Cross-sell): Model the triggers and pathways for account expansion. Develop propensity-to-buy models for add-ons (security, storage, backups) and build data-driven upgrade paths (e.g., VPS to Dedicated) to maximize customer wallet share. - ICP & Segmentation Modeling: Define and operationalize ICP segmentation using firmographic, behavioral, and intent signals. Create a decision framework to quantify ICP-specific retention, plan mix, and support costs. - Attribution & Measurement: Design multi-touch attribution (MTA) models calibrated with incrementality methods like geo-tests and holdouts. Improve full-funnel measurement from first touch through activation and retention. - Data Foundation & Reliability: Partner with Marketing Ops to ensure clean event tracking (GTM, dataLayer, UTMs). You'll be the guardian of the dataLayer, ensuring that what we see in GTM actually reflects reality in Snowflake. - Power BI Reporting & Governance: Own executive-ready reporting, including KPI scorecards and cohort views. Maintain semantic models using DAX, ensuring star schema design, query folding, and incremental refreshes for scalability. - Team Leadership: Help hire and coach a reporting and analytics team. Establish operating rhythms (WBR/QBR) and set standards for dashboard design, documentation, and storytelling. What Success Looks Like (6–12 Months) - Liquid Web utilizes a defensible LTV and payback model for all spend decisions. - LTV:CAC and payback are reported accurately by both ICP and channel. - Expansion metrics (upsell/cross-sell velocity and attachment rates) are visible and actionable for Marketing and Sales. - Power BI serves as the governed, trusted "source of truth" across the organization. Required Qualifications - Experience: 5+ years in Data Science or Growth Analytics within a subscription-based business. - Modeling: Proven track record building LTV, churn, and ICP segmentation models tied to revenue outcomes. - Revenue Growth: Demonstrated ability to analyze and optimize upsell/cross-sell funnels and product attachment rates. - Tech Stack: Advanced SQL and proficiency in Python/R. Expert-level Power BI (DAX and semantic modeling) and experience with Snowflake (or similar cloud warehouses). - Instrumentation: Familiarity with GTM tags, event schemas, and the flow of web/billing data into a warehouse. - Communication: Strong cross-functional leadership and executive-ready presentation skills. Nice-to-Have - Experience in hosting, cloud infrastructure, or developer-led products. - Pricing and packaging analytics (promo impact, add-on attachment). - Knowledge of privacy-centric measurement (server-side tagging, modeled conversions). Working Style - Builder Mindset: Comfortable moving from messy data to reliable, automated systems. - The "So What?" Specialist: You never deliver a chart without an insight. You understand that data without a recommendation is just noise. - Pragmatic: Knows when a calibrated "good enough" measurement beats a complex, fragile model. - Collaborative Leader: Able to influence without authority and level up a team through high standards. Work Environment The physical demands described here are representative of those that must be met by an individual to successfully perform the essential duties of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential duties. - While performing the essential duties of this job, the individual is regularly required to speak and hear - While performing the essential duties of this job, the individual is regularly required to use hands for keyboarding, gross manipulation, and fine manipulation - Works at a desk and computer screen for extended periods of time - Works in a traditional climate controlled office environment or from home - Works in a highly stressful environment dealing with a wide variety of challenges, deadlines and diverse employee population Disclaimer This job description is only a summary of the typical functions of the position. It is not intended to be an exhaustive or comprehensive list of all job responsibilities, tasks, or duties. Additional duties and tasks may be assigned as part of the job function. Liquid Web Inc. reserves the right to modify, interpret, or apply this job description in a way that best supports the organizational needs. The job description in no way creates or implies an employment contract. The employment contract remains “at will”. Equal Employment Opportunity Policy: Liquid Web is committed to offering equal employment opportunity without regard to age, color, disability, gender, gender identity, genetic information, marital status, military status, national origin, race, religion, sexual orientation, veteran status, or any other legally protected characteristic.

• Translate RFQ scope and OEB objectives into a solution blueprint spanning ingestion, storage, access, and analytics for DB/DC/Health benefits data • Design the secure, cloud-based Data Hub architecture (AWS, Databricks, Redshift, Glue, cataloging) optimized for performance, scalability, data quality, and resilience • Produce logical and physical data models for structured/semi-structured sources from third-party recordkeeper and internal systems, ensuring IV&V testability • Define standards for file formats, schemas, metadata, data versioning, and reconciliation patterns supporting analytics, IV&V, and audit readiness • Design and prototype ingestion pipelines, transformation jobs, and storage layouts aligned to architecture and IV&V requirements • Advise on query optimization, clustering/partitioning, workload management, cost controls, and monitoring for DB/DC/Health datasets in Redshift and Databricks • Support troubleshooting of data-quality or performance issues identified during IV&V and testing • Partner with Governance, Security, and IV&V leads to ensure delivery within the June–November term and support the November 2026 go-live goal.