GTM Data Science Leader

Autodesk; Denver (CO); On-site; USD 180,000 - 240,000; Full time

Job summary

Autodesk is seeking a dynamic Senior Manager, GTM Data Science to join the Go-to-Market Data & Intelligence (GDI) leadership team in Denver, CO. You will lead a diverse team of data scientists, own the portfolio and drive measurable business impact across customer engagement domains.

You will translate ambiguous business objectives into data science products, prioritize opportunities by value, and partner with Sales, Marketing, and Customer Success to embed analytics into GTM workflows.

Qualifications

• 8+ years of experience in data science, machine learning, or a related quantitative field.
• 3+ years of experience managing data science, ML, or analytics teams.
• Advanced degree in a quantitative discipline such as statistics, mathematics, economics, CS, engineering, or equivalent.
• Strong programming fluency in Python or R and SQL, with ability to engage in technical design and model-review discussions.

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, including customer retention and growth, conversion, engagement effectiveness, seller and marketer productivity, and resource allocation.
• 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, including roadmap reviews, technical reviews, launch readiness, outcome reviews, and escalation of material risks.
• Provide technical leadership that ensures the problem formulation matches the decision need - including when to use prediction, experimentation, causal inference, optimization, ranking, simulation, or other approaches.
• 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 ML, AI, experimentation, causal inference, and decision intelligence, and apply the appropriate methods to improve 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 to operate with business stakeholders.
• Conduct performance and talent reviews, provide candid 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.

Skills

Data science leadership

Team management

Python or R

SQL

Cross-functional collaboration

Education

Advanced degree in statistics / mathematics / computer science / engineering

Tools

Python

R

SQL

Job description

Autodesk is seeking a dynamic Senior Manager, GTM Data Science to join the Go-to-Market Data & Intelligence (GDI) leadership team in Denver, CO. You will lead a diverse team of data scientists, own the portfolio and drive measurable business impact across customer engagement domains.

You will translate ambiguous business objectives into data science products, prioritize opportunities by value, and partner with Sales, Marketing, and Customer Success to embed analytics into GTM workflows.