Senior Manager, GTM Data Science

autodesk; Toronto; On-site; CAD 130,000 - 200,000; Full time

Job summary

Autodesk is seeking a Senior Manager of GTM Data Science to lead a diverse team and own the portfolio, execution, adoption, and measurable business impact of data science initiatives across customer intelligence domains. You will translate business objectives into data science opportunities, prioritize by value, and drive adoption across GTM teams.

The role requires deep technical credibility, strong people leadership, and the ability to influence senior business leaders to improve customer

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 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 l

Job description

Position Overview

The Go-to-Market Data & Intelligence (GDI) organization empowers Autodesk teams to make better customer decisions using trusted data, analytics, and data science. The Data Science team develops Machine Learning products that help Sales, Marketing, and Customer Success teams determine which customers to engage, when to engage them, what actions to take, and how to measure the impact of those actions.

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. This role requires deep technical credibility, strong people leadership, operating discipline, and the ability to influence senior business and data leaders.

The data science team applies descriptive, predictive, and experimental methods to customer data to optimize decisions related to customer renewals, expansion, and segmentation. The data science team partners closely with GDI program management, analytics, ML engineering, and GTM teams and their operational partners to create customer intelligence experiences that are embedded in GTM workflows.

Success in this role is not measured only by the quality of models or insights delivered. You will be accountable for translating ambiguous business objectives into the right problems and products, prioritizing opportunities by expected business value, working across functions to integrate data science products into how GTM teams work, and demonstrating that those products improve decisions and business outcomes.

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 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 l