# Senior Manager, GTM Data Science **Company**: Autodesk **Experience**: senior **Job type**: full-time **Salary**: For Canada based roles, we expect a starting base salary between $158,000 and $231,000. **Category**: Data Science **Industry**: Technology **Apply**: https://autodesk.wd1.myworkdayjobs.com/en-US/Ext/job/AMER---Canada---Ontario---Toronto---University-Ave/Senior-Manager--GTM-Data-Science_26WD101004-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_5ea1094a-fc3 ## Description 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 ## Skills ### Required - Python - R - SQL - statistical modeling - machine learning - experimentation - causal inference - model evaluation ### Nice to have - B2B SaaS - subscription - customer lifecycle - commercial analytics - decision intelligence - next-best-action - propensity - prioritization - production machine learning --- Source: [Apply at autodesk.wd1.myworkdayjobs.com](https://autodesk.wd1.myworkdayjobs.com/en-US/Ext/job/AMER---Canada---Ontario---Toronto---University-Ave/Senior-Manager--GTM-Data-Science_26WD101004-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)