Jobs at Weekday, Inc.

Lead AI-enabled financial planning and strategic analysis, build data-backed business cases, refine unit economics, deliver executive-level insights, integrate ML into FP&A, and track KPIs to drive profitability and strategic decisions.

Review and edit tasks created by professionals across industries to improve clarity, structure, and alignment with quality standards. Provide actionable feedback, apply consulting frameworks, and ensure consistency with project guidelines. Work asynchronously as an independent contractor for a remote AI research project.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Design realistic civil litigation scenarios, draft attorney-level gold-standard responses, evaluate and grade AI-generated legal outputs using structured rubrics, provide detailed written feedback, and participate in onboarding and calibration. Minimum 20 hours/week; remote work across US, Canada, or UK.

Provide private equity expertise to evaluate and create high-quality financial and investment content for training AI systems. Review financial models, market analyses, deal documents, support due diligence, deal sourcing insights, and give structured feedback to improve analytical accuracy.

Provide advanced M&A financial modeling, valuation (DCF, comparables, precedent transactions), and transaction analysis to support deal structuring, due diligence, and strategic recommendations. Quantify synergies, test assumptions, build integrated operating models, and present findings to senior stakeholders in a remote contract role.

Serve as the internal customer advocate and pre-sales technical lead for finance-focused enterprise deals. Configure complex billing, pricing, metering, invoicing, and revenue recognition workflows; build demo environments; engage finance leaders; support RFPs and sandbox configurations; document solutions and handoffs; feed product and engineering with customer insights.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design institutional-quality, transaction-oriented financial modeling tasks and gold-standard reference models to train and evaluate AI. Build and review integrated 3-statement models, DCFs, LBOs, M&A and growth equity frameworks, perform diligence and sensitivity analyses, and document assumptions and methodologies while collaborating asynchronously with AI researchers.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Lead integration of AI-driven tools into tax provision, planning, and multi-jurisdictional compliance. Design ML/analytics solutions for sales and payroll tax, enhance regulatory filings, support audits, and collaborate with finance, legal, and technology teams. Monitor tax regulation changes, document AI tax models for transparency and governance, and continuously refine models for scalability, reliability, and compliance.

Serve as the internal customer advocate and technical pre-sales lead for finance-heavy billing and revenue recognition solutions. Translate complex billing and revenue requirements into configured demos, sandbox environments, and solution documents. Partner with Sales, Product, and Engineering to support discovery, RFPs, implementations, and handoffs for enterprise customers.

Design and implement AI/NLP solutions to automate insurance policy administration (issuance, endorsements, renewals). Translate policy language into models and rule engines, integrate with PAS, ensure regulatory compliance, monitor model performance, and support underwriting, operations, and exception handling.

Review and assess equity research, models, and market analyses to improve AI training data. Provide valuation, earnings forecasting, and investment memo feedback using long/short equity frameworks. Contribute to analytical materials, market sizing, and research summaries to enhance financial reasoning in foundational models.

Analyze and classify technical content for security vulnerabilities, apply pattern recognition to improve AI model threat detection, design and evaluate security problems, and collaborate asynchronously with domain experts to produce high-quality outputs.

Contribute transactional and corporate law expertise to train and evaluate AI models: design realistic deal scenarios, draft attorney-quality gold-standard responses, grade AI outputs with rubrics, provide written feedback to improve model reasoning, and participate in onboarding and calibration sessions.

Act as the internal voice of the customer, partnering with Sales, Product, and Engineering to design and configure billing, revenue recognition, and invoicing solutions. Lead discovery with finance leaders, build demo/sandbox environments, respond to RFPs, document handoffs, and create onboarding materials while advocating customer needs to Product and Engineering.

Design institutional-quality, transaction-oriented financial modeling tasks and gold-standard reference models to train AI. Build and review integrated 3-statement models, DCFs, LBOs, M&A and growth equity frameworks, perform diligence and sensitivity analyses, document assumptions, and collaborate asynchronously with AI researchers.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.