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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