Principal AI Scientist

Responsibilities:

• Guide the AI/Behavioral Science team in sophisticated Customer Experience modelling.

• Oversee prescriptive modelling life cycle and optimize the end-to-end pipeline.

• Partner with Customers, Retail Business Experts, AI Scientists, Behavioral & Cognitive Scientists, CX Designers and Solution Engineers.

• Understand business challenges, ask right questions, define precise problem statements, develop solution approaches that advance the state of the art, solve critical technical problems, lead the team and rapidly deliver highly effective solutions.

• Communicate business analysis and results to stakeholders using effective visuals and succinct documents.

• Nurture team members.

• Facilitate a positive, open work culture characterized by idea exchange, peer review, co-creation and relentless advancement.

Key Experiences:

• Implementing variety of AI techniques from basic descriptive statistics, hypothesis testing and feature transformation to advanced dimensionality reduction, supervised or unsupervised learning, model tuning and validation.

• Designing, training and evaluating highly sophisticated models. Modelling complex feature sets.

• Leveraging methods like Transfer Learning, Dimensionality Reduction and ProdtoVec.

• Complete AI lifecycle contribution problem identification & statement, solution approach development, data prep, analysis, visualization, algorithm development & implementation, results measurement, validation & presentation and improvement

• Effectively communicating complex concepts to technical and non-technical audiences.

• Working with colleagues across multicultural global offices.

Technical Expertise:

• Supervised and Unsupervised Machine Learning algorithms.

• Deep Learning techniques such as ANN, RNN, Transfer Learning, Explainable AI.

• Have experience working in NLP

• Statistical techniques such as Causal inferencing, significance testing, A/B testing

• Gradient Descent, Multi-objective optimization, Combinatorial Optimization.

• AI Search algorithms

• Linear Regression, Logistic Regression, RF, Clustering, SVMs, Boosting.

• Python and/or R

• TensorFlow, Pytorch, SkLearn

• SQL

• Data Visualization tools such as matplotlib, tableau etc.

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