Data challenge, 2015 (Released as a challenge at CoDS 2015)

In this data challenge, we released AMEO 2015, our data set which profiles undergraduates with varied backgrounds and pose these challenges-

Predictive Modeling Given a new student profile, can we predict his/her annual salary from historic data?

Recommendation Can we identify the key set of parameters in his profile changing which, s/he would get to earn a better salary?

Data Insights Can we understand what factors in the labor market determine one’s salary? Is it just one’s skills or there are other factors which influence the return in the labor market? What signals and biases enter the labour market? Can we make interpretable models or visualize features to understand what determines salary – for instance do kids from smaller towns get lower salaries? This can help us understand inefficiencies in the labor market, which will be extremely useful for policy making and constructing interventions.

Visualization Finally, can we visualize where and what jobs people get to get a quick and deeper understanding? Will all your creativity, show us visuals that will tell us something new and that we did not know.

This gives an opportunity to get to the bottom of labour market dynamics through a systematic study of employment data.

For more info, see here

For performance on our leaderboard, see here


Machine Learning Competition, 2011

The goal here was to solve the problem of efficient matching of people to jobs by merit: one that appeals most to the worker while delivering maximum value to their employer. We believe that there is a job for every individual. We wish to identify that job, match the individual to that job and facilitate the employment of the individual. On the other hand, we want to assist companies in acquiring talent which most closely matches the job requirement. We have a set of twin goals: equal job opportunity and meritocracy.

Our data shows that current ways of job-matching are deficient. They leave many employable candidates without jobs and companies without the right set of people to do the job. The solution to this problem will empower the ten million strong youth to steer their careers by merit and contribute towards the growth of our thousand billion dollar economy.

For more info, see here.