Bayesian Phd Jobs (NOW HIRING)

Intuit is looking for innovative and hands-on Staff AI Scientist to join the GTM Tech platform AI team.

Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches, targeted towards areas like marketing audiences, brand management, generative engine optimization, price optimization, creative generation and optimization, Digital twins etc. The world of marketing is changing dramatically. We are waiting for you to join us and do the best work of your life.

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 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 ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems. 

Qualifications

• 
  
  • 4+ years of industry experience with AI science
  • BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent
  • 4+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.
  • Prior experience / qualifications in Marketing platforms, media management. If you have a background in Cognitive sciences / Human Psychology (Consumers, Buyers, Supporters, Detractors, Group behavioral sciences), this is the role for you. 
  • Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization.
  • Proficient in NLP techniques, Explainable AI, and ML frameworks.
  • Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R.
  • Efficient in SQL, Hive, SparkSQL, etc.
  • Comfortable working in a Linux environment
  • Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
  • Quick learner, adaptable, with the ability to work independently in a fast-paced environment
  • Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.