Position Overview

Do you thrive in a fast-paced, high-energy environment? Are you a creative problem solver who enjoys building solutions and products using cutting edge technologies? Are you looking to collaborate with motivated individuals from diverse backgrounds? Do you have the drive to make things happen? If so, you are in the right place.

As a global leader in 3D design, engineering, and entertainment software, Autodesk helps people imagine, design, and create a better world. Autodesk enables better design through an unparalleled depth of experience and a broad portfolio of software to give customers the power to solve their design, business, and environmental challenges. In addition to designers, architects, engineers, and media and entertainment professionals, Autodesk helps students, educators, and casual creators unlock their creative ideas through user-friendly applications.

The Product Analytics Team is a centralized analytics team working closely with product line development teams to democratize analytics across all Autodesk products. You will be working closely with the Architecture Engineering Construction (AEC) Platform team to enable product leads to make data-driven decisions, strategize data collection, and perform deep analysis to provide insights about the users, design data, and usage of various AEC products and services.

Responsibilities

• Identify, analyze, and interpret trends or patterns in complex data sets

• Assist product teams in defining their data needs, structuring data, and designing data pipelines

• Guide product teams on how to define their data requirements and design the necessary structures and pipelines

• Collaborate with product teams to ensure their data needs are met efficiently and effectively

• Data exploration to verify instrumentations within design products

• Work with software development teams to deliver ad hoc reports and self-serve dashboards

• Create visually appealing reports, present and explain the analyses to stakeholders

• Conduct exploratory research for hypothesis testing and statistical analysis

• Drive and maintain a culture of quality, innovation, and experimentation

• Analytical advisor role that requires understanding of the theories and concepts of a discipline and the ability to apply best practices

• A common career stabilization point (AKA the “full-contributor” level) for Professional roles

• Require knowledge and experience such that the incumbent can understand the full range of relevant principles, practices, and practical applications within their discipline

• Solve complex problems of diverse scope by taking a new perspective on existing solutions and applying knowledge of best practices in practical situations

• Use data analysis, judgment, and interpretation to select the right course of action

• Apply creativity in recommending variations in approach

• “Connect the dots” of assignments to the bigger picture

• May lead projects or key elements within a broader project

• May also have accountability for leading and improving on-going processes

• Build effective relationships with more senior practitioners and peers, and build a network of external peers

• Work independently, with close guidance given at critical pointsMay begin to act as a mentor or resource for colleagues with less experience

Minimum Qualifications

• Bachelor’s or master's degree in Statistics, Information Systems, or suitable work experience

• 4+ years of experience in analytics, preferably in a business environment

• Proven ability to work collaboratively in cross-functional teams

• Track record of solving business questions using data

• History of querying and transforming data to compute metrics and inform decisions

• Advanced working knowledge in SQL queries

• Experience with data visualization tools and techniques.

• Strong problem-solving skills and attention to detail.

• Excellent communication skills to effectively present complex data insights to stakeholders.

Preferred Qualifications

• Experience with Business Intelligence tools such as Looker, Power BI, or Tableau

• Intermediate experience in Python or R for statistical analysis to solve complex problems

• Familiarity with AWS data services such as S3, Redshift, or EMR

• Experience working in Spark, PySpark or Hive/Presto environments