Jobs at Weekday, Inc.

Design, develop, and maintain scalable Python backend systems and APIs. Lead end-to-end development, optimize performance, participate in architecture decisions, conduct code reviews, resolve production issues, and mentor junior engineers.

Design and build scalable Python backends and APIs, integrate and deploy LLM-powered applications (chatbots, summarization, semantic search), optimize model performance and costs, develop data pipelines for training and inference, ensure code quality and CI/CD, collaborate cross-functionally, and mentor junior engineers.

Analyze complex business problems, conduct market and competitive research, evaluate business performance, identify growth opportunities, and develop data-driven strategic recommendations. The role supports operational improvements, business transformation, and implementation initiatives through structured frameworks, financial analysis, presentations, reports, and executive-level communication. Candidates need 1–7 years of consulting experience, strong analytical abilities, excellent communication skills, and a degree from a top-ranked university.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Design, develop, test, and maintain scalable software applications and systems. Write high-quality code, solve complex technical problems, collaborate with product and engineering teams, participate in code reviews and debugging, contribute to system architecture, and improve engineering processes. The role requires strong software fundamentals, production-scale experience, and familiarity with Databricks, distributed systems, and cloud infrastructure.

Expert reviewers will evaluate UK-law answers generated by two AI platforms. They will run identical prompts, compare outputs, score responses using a standardized rubric, and provide concise written feedback. The role requires at least two years of primarily law-firm experience in corporate, M&A, or transactional practice, UK qualification to practise, strong UK corporate and commercial law knowledge, fluent English, independence, and deadline management. The initial commitment is approximately 10 hours, with potential for additional work under an NDA.

Build and evaluate advanced Excel finance tasks for AI training data. Responsibilities include creating financial models and analyses, designing realistic finance scenarios, writing solutions, reviewing task accuracy, providing feedback, and collaborating with subject matter experts. The role requires advanced Excel skills, including formulas, PivotTables, dashboards, Power Query, Power Pivot, macros, and VBA, plus professional finance experience in areas such as private equity, investment banking, hedge funds, equity research, accounting, or FP&A.

Build and evaluate MLOps and ML systems tasks for frontier AI training data. Responsibilities include designing technical challenges, writing solutions and evaluation rubrics, profiling and optimizing GPU workloads, debugging distributed systems, improving model performance, and supporting high-throughput LLM inference. The role requires production experience with ML infrastructure, serving systems, GPU accelerators, JAX or PyTorch, and strong technical communication.

Design insurance pricing, reserving, forecasting, and portfolio-risk scenarios for AI model training. Create actuarial analyses and reference answers, evaluate AI outputs for quantitative and methodological accuracy, identify unsupported assumptions and errors, and provide structured feedback. The role requires explaining actuarial methods and business implications to technical and nontechnical audiences while participating in calibration and onboarding sessions.

Design and evaluate life insurance and annuity advisory, underwriting, policy-service, suitability, and replacement scenarios for AI training. Create expert reference responses, assess model outputs for product accuracy, mathematical correctness, suitability, tax treatment, and disclosure compliance, and provide feedback to improve model performance. Participate in onboarding and calibration sessions.

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.

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.

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, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

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.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.

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.

Manage and optimize end-to-end account reconciliations, intercompany eliminations, and chart-of-accounts governance. Identify discrepancies, perform variance analysis, and implement AI-driven automation and anomaly detection to reduce manual effort and accelerate close. Collaborate with cross-functional teams to resolve mismatches and improve financial data quality and reporting accuracy.

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.

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.