Senior Manager, GTM Data Science

The Go-to-Market Data & Intelligence (GDI) organization at Autodesk empowers teams to make better customer decisions using trusted data, analytics, and data science.

We are looking for a dynamic Senior Manager, GTM Data Science to join the GDI leadership team. You will lead a diverse team of data scientists and own the portfolio, execution, adoption, and measurable business impact of data science initiatives across customer intelligence domains.

Responsibilities

• Shape the multi-quarter Data Science roadmap for customer engagement use cases, aligned with GDI priorities and Autodesk business objectives
• Partner with senior leaders across Sales, Marketing, and Customer Success to identify and prioritize the decisions where data science can create the greatest customer and business value
• Translate ambiguous strategic questions into clear decision frameworks, analytical problem statements, intervention strategies, success metrics, and business cases
• Serve as a strategic thought partner to executives and cross-functional leaders; challenge assumptions, make clear recommendations, and communicate tradeoffs in a way that supports timely decisions
• Define success measures before development begins and hold the team and partners accountable for adoption and realized outcomes
• Own and actively manage a portfolio of Decision Intelligence initiatives across areas such as retention and churn, growth potential, upsell and cross-sell, segmentation, propensity and lead prioritization, license compliance, attribution, benchmarking, what-if scenarios, and next-best action
• Lead the end-to-end lifecycle from problem formulation and data readiness through modeling, validation, deployment, workflow integration, experimentation, impact measurement, and post-launch monitoring
• Make explicit portfolio tradeoffs, sequence work based on expected value and feasibility, and redirect or stop initiatives when evidence indicates that the opportunity is no longer compelling
• Partner with GDI program management, analytics engineering, data engineering, product managers, and federated analytics teams to define requirements, operating dependencies, delivery plans, and adoption mechanisms
• Establish clear operating rhythms and quality standards for the team
• Provide technical leadership that ensures the problem formulation matches the decision need
• Guide the team across statistical modeling, machine learning, experimentation, causal methods, propensity and uplift modeling, scoring and prioritization, and related advanced analytics techniques
• Maintain a high technical bar for data quality, leakage prevention, model evaluation and calibration, reproducibility, explainability, bias and fairness considerations, drift monitoring, and offline versus online performance
• Ensure machine learning products connect predictions or insights to specific actions, interventions, or policies and that their incremental impact can be measured whenever practical
• Stay current on advances in machine learning, AI, experimentation, causal inference, and decision intelligence, and apply the appropriate methods when to improve customer or business outcomes
• Lead, recruit, retain, and develop a high-performing team of data scientists with clear roles, standards, accountability, and career expectations
• Coach senior individual contributors and emerging leaders, developing both deep technical capability and the judgment required to operate effectively with business stakeholders
• Conduct performance and talent reviews, provide candid and actionable feedback, identify development opportunities, and build succession and hiring plans for critical capabilities
• Create an inclusive team environment that values rigorous technical debate, curiosity, continuous learning, cross-functional collaboration, and accountability for outcomes
• Build organizational capability beyond individual projects by improving reusable methods, decision frameworks, operating practices, and partnerships across the broader EDA organization

Minimum Qualifications

• 8+ years of experience in data science, machine learning, advanced analytics, or a related quantitative field, including significant hands-on applied experience
• 3+ years of experience managing data science, machine learning, or advanced analytics teams
• Advanced degree in a quantitative discipline such as statistics, mathematics, economics, computer science, engineering, or a related field, or equivalent practical experience
• Strong technical fluency in Python or R and SQL, with the ability to engage in detailed technical design and model-review discussions
• Demonstrated experience taking analytical or machine learning products from an ambiguous business problem through development, deployment, adoption, and measurable impact
• Strong knowledge of statistical modeling, machine learning, experimentation, causal inference, and model evaluation techniques
• Demonstrated ability to identify the right business problems to solve and translate business objectives into decision, measurement, and analytical frameworks
• Experience influencing senior stakeholders with data and analysis and leading cross-functional initiatives that depend on contributions from multiple disciplines
• Excellent written and verbal communication skills, with the ability to explain complex concepts, recommendations, risks, and tradeoffs to audiences ranging from technical practitioners to executives
• Strong judgment, ownership, and execution: you proactively identify gaps, resolve ambiguity, make clear recommendations, and remain accountable for outcomes across organizational boundaries

Preferred Qualifications

• Experience with B2B SaaS, subscription, customer lifecycle, or commercial analytics supporting Sales, Marketing, and/or Customer Success
• Experience building or scaling decision intelligence, next-best-action, propensity, prioritization, experimentation, or other analytical products embedded in operational workflows
• Experience with production machine learning and the operational lifecycle of models, including monitoring, retraining, and collaboration with engineering or MLOps teams
• Experience managing a portfolio of analytical products and making prioritization decisions based on expected customer or business value
• Experience developing senior technical talent, organizational capability, and operating mechanisms in a growing data science organization