Self-starter who can find the right problems to focus on Understanding of financial performance and appropriate analysisĮxcellent understanding of general business principles Understanding of experiment design methodology e.g., A/B testing Needs to be up to date with the latest tools and methodologies for analysing data.Įxperience with manipulating large data setsĮxperience with data visualization and data mining Will also need to define relevant KPI’s, build dashboards and provide reports and presentations to management so a commercial mindset is very important. This includes extracting, organising and structuring data in a way that will enable useful analysis. The data analyst will be responsible for analysing data and extracting insights that will facilitate management’s decision making as well as identifying new opportunities for growth. If you think you have what it takes then we would love to hear from you. We are looking for superstars in mathematics, physics, statistics, computer science and engineering who share our values. We are driven by complexity and curious to a fault.
Have face-to-face meetings to ensure they get the maximum return out of the activities.Join a growing team of data specialists who are working hard every day to build the future of machine learning, AI and predictive analytics. Sometimes, just spending time and talking with them helps to define what they need to deliver on rather than a paper-based request. Their technologists have the capacity to understand what your project needs. They also trained our staff which wasn’t part of the original contract.Įngage with their consultants and technologists. The engagement was regular and appropriate. They delivered based on what they said they would. I can’t think of anything they needed to improve. I was incredibly impressed with the way they did their work. Until they delivered what we signed off as an accurate delivery of a milestone or until we were satisfied with the different milestones, they wouldn’t bill us. It wasn’t milestone-bound but rather product-bound. We appreciated the way they structured the contract. It took them a very short time to understand the actual details of what we were looking for and to build a product that specifically met our requirements.
Our work is quite specialized and a lot of us have been working on it for many years. Elucidate AI is a machine learning solutions provider which offers data analytics and artificial intelligence (AI) solutions in finance, marketing. Their ability to understand our requirements and translate them into that program stood out to me. How did Elucidate perform from a project management standpoint?Įlucidate was incredibly professional.
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I recall it had a 94% success rate against our manual assessments. The results have received positive reviews as well. It reduced our human resource input in the process substantially. Our team felt the model was very successful. The solution improves every year but remains a standalone system that we can continue to use. The system we built can be used for as long as our selection criteria remain consistent.
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Maybe they could just broaden the communication within our business on what they’re doing.ĭo you have any advice for potential customers?īe open and transparent about what you’re trying to achieve so that they can work out ideas on how to solve your problems. Sometimes we thought was happening but ultimately, we knew it was. They did a lot of work in the background. They could up their communication, although it was all high. They’re smart and know what they’re doing. What did you find most impressive about them? It’s been a trying year now that we’re all working from home, and it’s been excellent under the circumstances. How did Elucidate perform from a project management standpoint? What evidence can you share that demonstrates the impact of the engagement?Įvery time we put in a machine learning model, we can assess quite easily, based on revenue, whether the model is successful or not. We worked together from November 2019 until June 2020. We got together and we found ourselves to be similar in culture from a business perspective. We worked with a project manager and a senior machine learning specialist. Then, once it was implemented, we’d have interactions on a weekly basis or more frequently to see how the product was working. They’d go away and then bring us solutions on how we could achieve it. We came with an idea of what we wanted to do. We knew what machine learning could do but we didn’t know how we could get there. We look at the population that we’re granting credit to and then put it into models where we can go crediting more people or, targeting the current people we’re granting it to, grant them higher values so that we can earn more. They helped us with machine learning development to maximize our lending opportunities.