Questions d'entretiens - Data engineer

I think estimating the probability P that a driver of Heetch would like to serve between two given locations could be one major problem. Eventually, Heetch would like to allocate drivers between two locations, based on this probability estimate. We can model p as a function of relevant factors: distance between points p, q, d=d(p,q), average income m_i of a driver, the corresponding standard deviation s_i of the incomes of the driver serving between p and q. We denote Heetch's revenue generated by these two points as R=R(p,q). Then, we can formulate the problem where we want to have a lower bound on R, but want to maximize the probability P of finding a driver willing to serve between p and q. To construct a suitable function P to maximize, we can study the covariance or correlation between each factor involved, and then form suitable f using this correlation coefficients. Next, we can maximize P, as a function of d(p,q), R, m_i, s_i, and solve the corresponding primal/dual problem as a constrained optimization problem on R>=R_min, for example.