Statistical Consult: Negative Binomial Regression with Random Effects

I need a statistician who can help me diagnose and fix a negative binomial regression incorporating random effects that is failing to converge. I am using R and the lme4 package, but I am open to having this work performed in STATA. The study is examining the use of parliamentary questions by Members of Parliament (MPs) from a national parliament between 1999-2016. I am using MP-level observations. The dependent variable (QW) is the number of written questions each MP asks in a particular calendar year (i.e. 2001, 2002, 2003). It is count data, so I am employing a negative binomial regression. Since I have multiple (5) observations for each MP from each of the parliamentary sessions, I am employing MP-level random effects. As a result, I have a total of 9,528 observations grouped into 1,371 groupings. The basic model I am attempting to estimate is: basemod <- ([login to view URL](WQ ~ VM + Opp + Jpart + LS_Exp + uCM + uJM + log(QDays) + (1|ID), data=qdata, verbose=T, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))) WQ = the number of written parliamentary questions asked in a given year VM = the vote margin that the MP won their last election by Opp = Binary variable: 1= member of an opposition party (not in the government) Jpart = Binary variable: 1= member of a junior party in the ruling coalition (part of the government) LS_Exp = Years of parliamentary experience uCM = Years of experience as a Cabinet Minister uJM = Years of experience as a Junior Minister QDays = the number of days allotted to parliamentary questions in a given year ID = unique identifier for each MP When I attempt to run this, I get the following warnings: Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0011218 (tol = 0.001, component 1) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue - Rescale variables? As a result, I have tried to scale the model back to the basics (just the dependent variable and the random effects) and start building it up, but I get convergence failure even when I run a bare bones model with just the dependent variable and the random effects, I still get convergence failure. minmod <- ([login to view URL](WQ ~ (1|ID), data=qdata, verbose=T, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))) Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.174211 (tol = 0.001, component 1) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue - Rescale variables? Can you help fix this? I have access to STATA and am happy to have this work performed in STATA as well, as long as you can provide me with the code. I know that some freelancers bid on projects without reading the full description. To confirm that you have read this and understand the task, can you please make the following sentence the first line of your response: "I understand NBR using FE and I can help with this problem."