If its an MVP, why care to spend on a logo? Just create a text one with a nice font and bg color. Segoe UI or Open Sans look good. There are also some websites which can generate logos for you.
If you want someone to do it, put it up on freelancer.com and get it done for 10-15 bucks or fiverr as someone already mentioned.
Thanks bruce. Yes, Linkedin is for sure the professional destination. Users are slightly hesitant to give out linkedin data, Facebook has a 5x larger userbase and people tend to fill minimal data such as job title and education, so the idea is to build and use a professional graph so as to interpret relevant information to the data and quantify it. There is some useful info such as interests, likes, groups etc which give vital information too.
The plan is to integrate all possible social sites and aggregate the professional data out of it.
Special thanks for mentioning Credit Score. The algorithm we are building is pretty much like a Credit Score algo. Not just because we show a score for users, but because we use factors learned and deduced from similar other profiles and use it to quantitatively predict the likelihood of a fit (risk in case of a credit score). It would be ideal to use larger datasets for training, however its still in beta and the algorithm is being updated continuously.
Thanks for the clickable link too :)
Hi, thanks for the comment. There is some ongoing development on predicting couple of main things:
1) Would a particular person be potentially a good candidate for certain role. This is done by using similar other profiles as a training set and use those factors to predict early on if someone would be a fit. e.g. People who have studied in a university of reputation X, have Y yrs of experience and have Z skills go on and achieve position 'A'. These factors would be used to predict the users who share the traits and would likely land in a similar position.
2) Predikt the probability of job hopping, likelyhood of someone being a passive job seeker, potential salary ranges the candidate falls into.
Right now, Linkedin and facebook integration are functional, certainly it would be more useful to add all possible networks. Github, Stackoverflow would be next. Some recruiters have approached and would like to use this for screening their applicants.
Thanks. It predicts your professional expertise in specific domains for specific roles based on data from user's profile as well as similar other profiles. Sample representation: "Score of 67 in Data Science domain for Data Scientist Role".
Multiple factors such as education, work etc. go into algorithm (much more being added). Klout, Kred etc measure social influence based on how much you engage and interact on the web.