Senior Manager, GTM Data Science

Autodesk•Toronto, ON

6d•CA$158,000 - CA$231,000•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

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 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