We are looking for a Staff Machine Learning Engineer to join our team. The ideal candidate will set the technical direction of their team to build and deploy ML solutions for pricing optimization - a high visibility and key initiative for TRR. Pricing optimization is core to our profitability and to ensuring that our users get the most value from consigning and buying with us. We are looking for someone who will find passion in the application of advanced optimization techniques to millions of items and be at the forefront of innovation in e-commerce. The ideal candidate for this role will be a thought leader and forward thinker who is eager to drive high impact efforts in close collaboration with a talented team of machine learning engineers, business stakeholders, product, analytics and software engineering partners.

In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the retail market. First, an adaptive clustering-based customer segmentation framework is proposed to categorize customers into different groups to enable the effective identification of usage patterns. Second, customized demand models with important market constraints which capture the price-demand relationship explicitly, are developed for each group of customers to improve the model accuracy and enable meaningful pricing. Third, the multiple pricing based demand response is formulated as a profit maximization problem subject to realistic market constraints. The overall aim of the proposed scalable and practical method aims to achieve 'right' prices for 'right' customers so as to benefit various stakeholders in the system such as grid operators, customers and retailers. The proposed multiple pricing framework is evaluated via simulations based on real-world datasets.

In recent years, artificial intelligence (AI) and machine learning (ML) technologies have successfully penetrated many industries, ranging from healthcare to automotive. However, retail is particularly susceptible to adopting various ML applications as data is this industry's driving force both in the physical and e-commerce realms. For example, an AI-powered recommendation engine has become a must-have among retailers, enabling them to boost customer experience and drive revenue. Nowadays, retailers are keeping their ears to the ground to be the first to adopt the next industry changer. Of course, there are attention-grabbers like computer vision-powered robots that autonomously take care of inventory, or facial recognition cameras that detect customers' emotions. However, the next big thing, ML-powered pricing optimization, seems to sacrifice flashiness for the sake of a significant competitive advantage and return on investment.

Data analytics helps people better understand their business and see weaknesses and inefficiencies more clearly. Analytics can also increase revenue through pricing optimization or analyzing margin or costs to improve the efficiency of the manufacturing process. Many internal audit departments, however, are just getting on board with this trend. Smart Business spoke with Kirstie Tiernan, data analytics managing director for BDO, Chicago at BDO USA, LLP, about data analytics, especially as it relates to internal audit and why artificial intelligence (AI) adds even more value. How are employers developing programs around data analytics? Data analytics requires people, tools, infrastructure and IT, and it's an involved investment.

Check the answers from KDnuggets Editors to these questions (and one more): 21 Must-Know Data Science Interview Questions and Answers Now that the Data Scientist is officially the sexiest job of the 21st century, everyone wants a piece of the pie. That means there are a few data posers out there. People who call themselves Data Scientists, but who don't actually have the right skill set. This isn't always done out of a desire to deceive. The newness of data science and lack of a widely understood job description means that many people may think they are data scientists purely because they deal with data.