About the position
Autodesk is leading the transformation of the AEC industry, integrating AI technology into our products. We're enhancing our applications with cloud-native capabilities, including data at scale, edge computing, AI-based solutions, and advanced 3D modeling and graphics. This innovation is happening across our flagship products—AutoCAD, Revit, and Construction Cloud—and Forma, our new Industry Cloud.
As a Principal Data Scientist on the AEC Solutions team, you will join a team of technologists to help build cutting-edge foundation models and generative AI tools for the AEC industry. You will collaborate across organizations with a versatile group of AI Researchers, ML Engineers, Software Architects, and Experience Designers to generate and interpret design data that can augment design and engineering workflows. The ideal candidate is someone who isn't afraid of technical complexity and is constantly looking for ways to leverage the latest and greatest advancements in the field of data science to deliver next-generation experiences for our AEC customers.
Responsibilities
- Drive strategic data science initiatives, influencing how machine learning and analytics shape key business decisions.
- Analyze large-scale datasets, extract deep insights, and develop advanced data visualization strategies that drive measurable impact and provide measurable recommendations.
- Architect scalable data pipelines to organize, process, and clean large batches of text, image, and geometric data for downstream analytics and machine learning.
- Lead experimentation and advanced statistical modeling, designing robust methodologies to validate hypotheses and optimize business outcomes.
- Develop analytical solutions and interactive dashboards that integrate data from various sources, including relational databases and AWS.
- Develop, deploy, and operationalize machine learning models, ensuring scalability, reliability, and real-world effectiveness.
- Mentor and guide other data scientists, fostering a culture of knowledge sharing, collaboration, and continuous learning.
- Collaborate cross-functionally with engineering, product, and business teams to define and execute data-driven strategies.
- Stay ahead of emerging trends in AI and data science, proactively identifying opportunities to integrate cutting-edge techniques into business applications.
- Define and promote best practices and frameworks for data science workflows, model deployment, and responsible AI usage.
Requirements
- A Master's degree (or higher) in Computer Science, Business Analytics, Data Science, Management Information Systems, Engineering, or another quantitative discipline OR a Bachelor's degree with extensive industry experience.
- 5-7 years of relevant work experience in data science, machine learning, or AI, OR extensive applied research experience through a doctoral program.
- Strong understanding of statistical and machine learning techniques, including classification, regression, dimensionality reduction, regularization, clustering, multivariate analysis, feature engineering, and hyperparameter tuning.
- Proficiency in Python for large-scale data analysis, with experience in open-source data science toolkits such as Pandas, SciPy, and Scikit-Learn.
- Hands-on experience with data visualization tools like Matplotlib, Seaborn, Plotly, or BI platforms like Power BI or Tableau.
- Proficiency in distributed computing frameworks like Spark or Ray for scalable data processing.
- Experience with relational data modeling and SQL, including data retrieval, transformation, and optimization for analytical applications.
- Experience with model monitoring and Tasknexa/CD pipelines, including model versioning, automated retraining, and performance tracking in production environments.
- Expertise with cloud computing and MLOps, including AWS (S3, Lambda, SageMaker, Gigflowx) or equivalent cloud platforms (GCP or Azure) with knowledge of model deployment best practices.
- Strong analytical and problem-solving skills, with the ability to translate theoretical concepts into practical solutions by designing, prototyping and deploying scalable machine learning solutions.
- Excellent communication and storytelling skills, including the ability to distill complex insights into actionable business recommendations through written, oral, and visual presentations.
Nice-to-haves
- Background in Architecture, Engineering, or Construction.
- Deep learning experience, with hands-on proficiency in frameworks like TensorFlow or PyTorch for designing, training, and deploying neural networks.
- Experience leading cross-functional teams and mentoring data scientists, fostering a culture of innovation and knowledge sharing.
- Familiarity with responsible AI principles, including bias mitigation, explainability, and ethical AI practices.
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