The Top Technology Trends and Their Impact on Data Science, Machine Learning and AI
- Not only enormous numbers of data scientists and engineers are urgently needed across the various industries, but product designers, behavioral economics experts, strategy people, lawyers, consumer coaches, and so on.
- There will not anymore be “a customer analytics data scientist,” “a user experience designer,” “a business person,” or “a machine learning engineer.” The areas amalgamate, and multidisciplinary teams become the new standard.
- Data scientists and machine learning experts that can bridge the complex technical methods with the business value to deliver the experience are in demand, and it will accelerate your career.
- The impact is that the whole data science work is moving entirely into distributed cloud solutions, but the execution needs sophisticated machine learning algorithms.
- My opinion on the impact: Data scientists will mainly be users of such infrastructure, but that allows them to work in an effective collaborative team independent of the physical location.
We're hiring for Senior Product Designer at The Muse
- ResponsibilitiesWorking closely with your squad’s dedicated Senior Product Manager, Tech Lead, engineers, and stakeholders, you’ll design and maintain a suite of products and features that drive both user delight and business value.Ownership: You will own the company’s Candidate-facing designs, improving upon existing products and conceiving new ones.Design Process: You’ll refine our methodology for conceiving, executing, releasing, and maintaining The Muse user experience.UX Strategy: You’ll own your squad’s product-design roadmap and other strategic artifacts, such as customer journey maps, user flows, and personas.Collaboration: You will gather requirements and input from members of your team as well as from other internal stakeholders, particularly Content and Marketing.Design System: To ensure design and dev process efficiency, you will create, organize, and maintain patterns in a design system.User Research: You’ll develop a sustainable qualitative research plan that garners ongoing insights from our Millennial and Gen-Z job-seekers; you’ll leverage our robust suite of quantitative data to make informed design decisions.Mentorship: You’ll hone our organization’s design acumen by coaching other designers, stakeholders, and squad members on design process, philosophy, and execution.Execution: You’ll deliver to market design that elevates The Muse brand and bring career development to the next level.
Bayesian modelling for tennis player ranking
- In this article, we’re going to use Bayesian modelling and MCMC to predict the outcomes of tennis games and in doing so, create a ranking of tennis players.
- The approach to modelling this problem is centred around the idea that a tennis player has an underlying skill score that determines their ability to win a match.
- Worth noting that since we’re treating this as a Bayesian modelling problem, everything becomes probabilistic and all skills and surface bonuses come with an uncertainty that should model the real variance that different players have.
- The likelihood states that a given tennis match is distributed as a binary Bernoulli random variable where the parameter p is determined by the sigmoid of the difference in player abilities.
- This is because likelihood is only dependant on the difference in player_ability and surface_bonus so any prior on the mean would only result in a poorly constrained problem.