UK TOP TECH JOBS: ZLEMMA SCORES YOU
to score them for each of these top jobs, mapping their skills and experience to what the companies are looking for. Students get a list of their scores and see which dream job they match to best.
“Too many students who graduate with technical degrees are very qualified, but don’t know what kind of jobs they should explore,” says Ashwin Rao, ZLemma’s co-founder & CEO. “What we’re doing is trying to broaden their option pool and to help them make better, more informed choices.”
Figuring out what kind of jobs are right for you after graduating is hard. But students in the UK got a big helping hand last month from a Silicon Valley startup that’s using algorithms to match technical talent to jobs where they would excel.
Computer science students and professors at top London universities worked with ZLemma, to shortlist 25 of the top “dream” tech jobs in the UK. A number of interesting career opportunities were selected ranging from programming positions at Google and Facebook to statistician at Atass and games developer at Frontier Games.
When computer science, maths or any STEM student sign up on
ZLemma.com, the startup uses its proprietary algorithms
Ashwin knows first-hand how hard career decisions can be for graduates with science, technology, engineering or maths (STEM) degrees. When he got his Ph.D. in Algorithmic Maths from the University of Southern California, he had no idea what kind of job to get. Most academic departments are designed to help Ph.D. graduates get academic jobs and have little guidance on opportunities outside academia. And university career services departments usually don’t focus on jobs that require specialized skills like data scientist or quant finance.
Ashwin ended up joining Goldman Sachs as a strategist on their quant finance team in New York, also spending a couple of years in London. His father, in India, couldn’t understand what his son
was doing with a maths degree at a bank. “He thought I had gotten a Ph.D. just to be a bank teller,” Ashwin says, laughing.
ZLemma’s algorithms take into account educational qualifications, the places where people have worked, competitions and prizes they’ve won and even contributions they’ve made to social forums like GitHub. They then model jobs based on the skills they need and the kind of background companies are looking for. Their scoring algorithm, inspired by Zorn’s lemma, calculates how well the candidate matches the job.
It sounds easy, but it’s not. Ashwin & the ZLemma team spent months crafting & fine- tuning the algorithm to work with industries as different as software engineering and high finance. Today, ZLemma works with brand-name employers such as Goldman Sachs, Two Sigma and Walmart Labs.
Sign up takes just a couple clicks and a few seconds. ZLemma connects with LinkedIn profiles to pull data so users don’t have to manually enter it. New users start seeing scores and jobs they match to in less than a minute. Hiring managers at ZLemma’s client companies immediately see high-scoring candidates for their jobs, who get a boost if they apply.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146