Principal AI Scientist
Role: Principal AI Scientist
Experience: 3 to 5 years
Qualification: PhD in AI, Comp Science, Math or Stats from exceptional institutions
PRISTINE IS LOOKING FOR – Principal AI Scientist
Pristine is looking for you. Pristine’s pioneering AI technology delivers an optimal experience for each individual customer of our retail and CPG clients. To survive & thrive, our clients need to transform their businesses rapidly and deliver purposeful customer experiences. This customer experience covers Product, Presentation, Pricing and Fulfillment. Every day, our SaaS platform receives and learns from 30 million+ new customer interactions, representing 12,000 stores/websites and 30 million customers. We are looking for exceptional Principal AI Scientists to rapidly advance our solutions and strengthen our leadership. For this role, we are looking for leaders with solid foundation in AI, Mathematics, or Computer Science with proven solution life cycle implementation experience.
▸ Contribute to prescriptive modeling life cycle and optimize the end-to-end pipeline.
▸ Partner with Customers, Retail Experts, AI Scientists, Behavioral & Cognitive Scientists, CX Designers, and Solution Engineers.
▸ Understand business challenges, ask the 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 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, constructive peer review, co-creation, and relentless advancement.
▸ Designing, training and evaluating 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, measurement, validation & presentation, and improvement.
▸ Effectively communicating complex concepts to technical and non-technical audiences.
▸ Working with colleagues across multicultural global offices.
▸ GAN, ANN, RNN, GNN, Transfer Learning, Generative AI, NLU, Combinatorial Optimization.
▸ Causal Inferencing, A/B Testing.
▸ AI Search algorithms.
▸ Gradient Descent, Multi-objective optimization, Combinatorial Optimization.
▸ AI Search algorithms
▸ Python and/or R.
▸ TensorFlow, Pytorch, SkLearn.