Senior Data Scientist

Iterable is the AI customer engagement platform, built for enterprise scale, loved by teams, and trusted by global brands like Redfin, SeatGeek, Priceline, Calm, and Box. Our platform empowers organizations to activate customer data from any source, design seamless cross-channel experiences, and optimize engagement with Nova Intelligence, the AI layer of our platform, all with enterprise-grade security and compliance. Today, nearly 1,200 brands across 50+ countries rely on Iterable to drive growth, deepen customer relationships, and deliver joyful customer experiences.

Our success is powered by extraordinary people who bring our core values to life every day: Be an Owner, Growth Mindset, Run as One, and Transparency. And the market has noticed: G2 recognizes Iterable as a Leader globally, spanning the Americas, EMEA, APAC, ANZ, and Latin America, with category leadership in Personalization, Marketing Automation, Push Notifications, and Mobile Marketing, and a 4.5/5-star rating from hundreds of users. Gartner named Iterable a Challenger in the Gartner Magic Quadrant for Multichannel Marketing in both 2024 and 2025. We’ve also earned TrustRadius Top Rated honors in 2024 and 2025, were named a TrustRadius Buyer’s Choice for 2026, and were featured in Snowflake’s Modern Marketing Data Stack 2026.

With a global presence that includes offices in San Francisco, Denver, London, Sydney, and Lisbon, plus remote employees worldwide, we are committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and encourage you to apply. Learn more about our story and mission on our Culture and About Us pages. Let’s shape the future of customer engagement together!

As a Data Scientist in Business Operations, you will lead the machine learning and predictive intelligence strategy across the enterprise.

This is a strategic leadership and high-impact IC role focused on driving bottom-line impact through production-grade predictive models. Instead of producing static analytics or isolated ad-hoc experiments, you will own the end-to-end design, development, and deployment of core business models—specifically propensity-to-buy/upsell, user engagement scoring, and product-usage-to-paid-conversion engines. You will directly influence Go-To-Market (GTM) execution, product-led growth (PLG) strategies, and customer retention by embedding real-time predictive scores directly into operational workflows.

Core Responsibilities

1. Predictive Modeling & Machine Learning Strategy (40%)

• Usage-to-Conversion Modeling: Architect machine learning models that analyze product telemetry, feature adoption, and user behavior to predict product-led conversion milestones (e.g., PQL identification, free-to-paid transitions, self-serve upgrades).

• Propensity & Lifetime Value Engines: Build and deploy propensity-to-buy, expansion/upsell, and churn risk models to prioritize pipeline for Sales, Account Management, and Customer Success teams.

• Engagement & Health Scoring: Design multi-dimensional user and account engagement algorithms combining behavioral, transactional, and firmographic signals into dynamic health scores.

• Algorithm Selection & Tuning: Implement advanced ML methods (e.g., XGBoost, LightGBM, Random Forests, survival analysis, neural nets) optimized for imbalanced business datasets.

2. Operational MLOps & Productionization (30%)

• Production Deployment: Own the deployment of production-grade models into cloud infrastructure (e.g., AWS SageMaker, Databricks, Snowflake ML) for real-time and batch scoring.

• Operational Integration: Partner with Data Engineers to push model outputs directly into business systems (Salesforce, Customer Success platforms, Marketing automation) via Reverse-ETL pipelines for automated workflows.

• Model Observability & Retraining: Establish MLOps frameworks to track feature store integrity, model drift, prediction accuracy, and automated retraining triggers.

3. Experimentation & Business Impact (20%)

• Causal Inference & Impact Measurement: Design Causal Inference frameworks and A/B/N test methodologies to validate the incremental revenue impact generated by predictive scores and GTM plays.

• Executive Alignment: Translate complex statistical outputs and feature importances into clear, actionable business strategies for GTM, Product, and Finance executives.

• GTM Playbook Optimization: Work directly with BizOps Managers to turn model outputs into operational triggers (e.g., automated outreach, targeted in-app prompts, dynamic trial extensions).

4. Technical Leadership & Governance (10%)

• Data Science Infrastructure: Evaluate, select, and manage the BizOps ML stack, feature stores, and modeling infrastructure.

• Ethical AI & Governance: Ensure models are fair, explainable (using SHAP/LIME values), transparent, and compliant with enterprise security and data privacy standards.

• Technical Mentorship: Code-review, train, and mentor Level 1–3 Data Scientists and Analytics Engineers on ML best practices, feature engineering, and statistical rigors.

Qualifications & Experience

• Total Experience: 6+ years of industry experience as a Data Scientist or ML Engineer, with at least 3+ years embedded in or supporting BizOps, Revenue Ops, Product Analytics, or Growth teams.

• Domain Expertise: Proven track record of building and deploying propensity, retention, or PLG conversion models that directly generated measurable business value.

• Business Acumen: Deep understanding of B2B SaaS mechanics (PQL, ARR, CAC Payback, NRR, Trial-to-Paid Funnels, Cohort Retention).

• Education: Degree in Computer Science, Statistics, Applied Mathematics, Data Science, or a related quantitative field.

What Success Looks Like

• Month 3: Audit existing product telemetry and historical GTM data, establishing a unified feature store for account-level engagement signals.

• Month 6: Deploy a production usage-to-conversion (PQL) model integrated into Salesforce, increasing Sales team conversion rates on product leads by 25%.

• Month 12: Build an enterprise-wide propensity and churn prevention suite with continuous MLOps monitoring, measurable incremental revenue lift, and automated retraining.

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