Senior AI Scientist

Overview

Responsibilities

• Practices leadership and communication skills to influence teams and to evangelize AI science across the organization
• Collaborates with stakeholders to define success criteria and align ML model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
• Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team's work, and holding the team accountable for high quality code, git, design, costs and implementation standards
• Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them model-ready. You need to be willing and able to do your own ETL and design/build featurization.
• Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
• Runs A/B tests to draw conclusions on the impact of your team's work and communicates results to peers and leaders
• Communicates with partners to ensure successful delivery and integration of DS solutions.
• Proactively researches, explores, and enables new AI/ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems.
  

Qualifications

• MS or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.).
• 2+ years of experience with data science for PhD and 5+ years for Masters.
• Experience in modern advanced analytical tools and programming languages such as R or Python with scikit-learn.
• Efficient in SQL, Hive, or SparkSQL, etc.
• Comfortable in Linux environment
• Experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, recommender systems, sequential pattern discovery, and text mining.
• Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences