• Provide technical support for students over an online community forum • Answer questions around course content over the forum and facilitate content for students who get stuck in learning • Host one weekly live virtual session to offer direct, real-time support to students. • Redirect learners to appropriate support channels and escalate issues to the Mentor Operations team when needed

• Design, build, and evaluate classical machine learning models for business-critical use cases (classification, regression, ranking, anomaly detection, time-series forecasting). • Apply probabilistic and Bayesian modeling techniques to quantify uncertainty and inform decision-making under uncertainty; leverage tools like PyMC and PyMC-Marketing for Bayesian workflows. • Perform rigorous EDA, feature engineering, and data wrangling on large structured and semi-structured datasets using Python and SQL. • Collaborate with data engineers and analytics engineers to source, clean, and validate data pipelines feeding ML workflows. • Develop, track, and communicate model performance metrics; identify degradation signals and recommend retraining or improvement strategies. • Translate business questions into well-framed statistical problems and present findings clearly to technical and non-technical stakeholders. • Maintain clean, reproducible, and well-documented code and notebooks following team engineering standards.

• Innovate and improve machine learning models for financial progress • Collaborate on designing, developing, and deploying machine learning models • Partner with business leaders and technical experts to develop new data sources • Apply models with sound risk management

• Analyze product usage, deal, financial, and billing data to model pricing, packaging, and monetization scenarios. • Design frameworks to assess willingness to pay, customer value, and price realization across customer segments and product lines. • Conduct research on pricing strategies, packaging models, and monetization approaches across comparable and adjacent markets. • Lead pricing and packaging strategy for new product launches, partnering with Product, GTM, and Finance to define commercial models • Build value-based pricing and econometric models to quantify elasticity, customer ROI, and lifetime value. • Design and execute pricing experiments • Synthesize quantitative analysis, customer research, and competitive intelligence into executive recommendations that influence pricing strategy. • Drive customer and prospect research to understand purchasing behavior, value perception, and pricing friction. • Partner closely with Product, Analytics, Finance, Sales, Marketing, and Legal to deliver end-to-end pricing initiatives. • Define KPIs and dashboards that measure pricing performance and identify optimization opportunities. • Continuously evolve our pricing and packaging strategy to improve ARR growth, customer adoption, and value realization.