Machine Learning Engineer/Data Scientist @ Autodesk

Position Overview

Autodesk is seeking aMachine Learning Engineer/Data Scientist to join our Sales Data Science team.We build innovative data products and machine learning solutions for Autodesk ssales teams. In this critical role, you will work alongside product development,product managers, and data engineers to tackle fundamental data gathering,management, and understanding of our APAC sales region. The ideal candidate isa strong communicator and has experience as a Data Analyst or Data Engineer whohas strong Data Science leanings and has built out multiple analytic models andmachine learning algorithms before. Data, automation and advancedanalytics technologies are drastically transforming our sales team in APAC andthis person will be the data scientist in charge of overviewing our datascience practice in Singapore, India, China, Korea, Australia, and New Zealand

Responsibilities:

• Designing and implementing Machine Learning models and algorithms that enable account selection, customer targeting, and process improvements for the sales teams in APAC
• Develop and maintain model deployment pipelines for many types of machine learning including supervised and unsupervised learning as well as CNNs, RNNs or other deep learning algorithms
• Working closely with data scientists, domain experts and sales team, both to understand model performance management requirements and design suitable inferencing instrumentation systems and practices that meet them
• Designing and implementing outbound data engineering pipelines that serve curated datasets to business intelligence and reporting
• Designing integration solutions including applications as needed to deliver inferencing outcomes or curated data sets for consumption and action
• Ensuring your model deployment, outbound data engineering and integration pipelines are architecturally and operationally integrated with inbound ingestion and contextualization pipelines designed by your peer domain architects
• Delivering and presenting results to sales leaders regarding their business, forecast, pipeline, and potential customers

Minimum Qualification

• Advanced degrees in computer science and data science strongly preferred, though an equivalent level engineering, data science or mathematics degree, a technical undergraduate degree and relevant experience will also be considered
• 8 plus years of relevant work experience
• 3 years of experience working with data scientists in a data engineering or production machine learning inferencing capacity, working with various types of supervised and unsupervised learning algorithms for classification, recommendation, anomaly detection, clustering and segmentation, as well as CNNs, RNNs or other deep learning algorithms
• 5 years of full-stack experience developing large scale distributed systems and multi-tier applications
• 5 years of programming proficiency in, at least, one modern JVM language (e.g. Java, Kotlin, Scala) and at least one other high-level programming language such as Python
• 2 years of production DevOps experience
• 3 years of programming on the Apache Spark platform, leveraging both low level RDD and MLlib APIs and the higher-level APIs (SparkContext, DataFrames, DataSets, GraphFrames, SparkSQL, SparkML).
• Demonstrated deep understanding of Spark core architecture including physical plans, DAGs, UDFs, job management and resource management
• At least 1 year of implementation experience with Apache Airflow, and a demonstrated expert level understanding of both segmented and unsegmented Directed Acyclic Graphs and their operationalization
• Experience working with Neo4J and a demonstrated ability to lead architecture efforts for its implementation
• Strong technical collaboration and communication skills
• Very strong communicator
• Passion for sales and customer segmentation
• Proficiency with functional programming methods and their appropriate use in distributed systems
• Proficiency with AWS foundational compute services, including S3 and EC2, ECS and EKS, IAM and CloudWatch
• Proficiency with Sagemaker, Kubernetes, and Docker

Preferred Qualifications

• Experience with data science toolkits like: R, Pandas, Jupyter, scikit, TensorFlow, etc.
• Experience with Sagemaker and data pipelines in AWS
• Familiarity with statistics concepts and analysis, e.g. hypothesis testing, regression, etc.
• Experience building dashboards in platform: Power BI, Tableau, Qlik, Looker, etc.
• Salesforce experience is a plus