Director of a public sector data science team. My department budget is about $500k, not including personnel costs.
Because we're a public entity, we don't really have a discretionary petty cash account or whatever. When I buy lunch for my team, it's out of my own pocket.
Your script is fine. Aside what you've scripted, limit any answers to their questions as yes or no. Refer to HR whenever you can.
Once you terminated someone, there's nothing to be gained from continued engagement with them. And a whole lot to lose if you trip up and say the wrong thing.
It's a crappy position to have to do hiring when there are internal candidates who won't be selected. It's not so much that the people who will be unsuccessful will be upset with you; rather, it's their co-workers who are upset with you on their behalf. And you can't really talk about it to candidly describe why the person hired was a better fit or whatever than the unsuccessful applicants. It sucks for everyone.
Don't be too hard on yourself, OP. Having multiple qualified internal candidates is difficult when you can only hire one.
We have team members complete a "pre-meeting reflection" and then the manager writes up a "post-meeting reflection." The team member can write whatever about their performance and then the manager can dispute any item in their part.
If you're going to give someone something higher or lower than effective, then the manager needs to assemble artifacts that show evidence of a higher or lower rating. So managers get an annual lecture about centrality bias and then most rates nearly everyone as effective because it's the path of least resistance.
Here's likely how these people have stayed employed for so long: documentation doesn't exist anywhere on data infrastructure that is not well-designed and so it would take months for a new competent data engineer to figure out how everything works together. So basically the organization is being held hostage by its lazy contractors.
The only way to solve this problem is to hire new people who spend 3-6 months learning the new systems and then terminate the existing team. You'll be hiring redundancies. It's a very expensive problem but the least bad option. The alternatives are to allow the status quo to continue in perpetuity or fire them all at once and hope the new team can get things back online quickly. Without knowing exactly how complicated the data system is, it's impossible to evaluate that risk.
More money and a chance to impact the organization at a greater scale than I could as an IC.
As someone who taught undergraduate econometrics at a Big 10 university for a bit, you absolutely need that mathematics background if you're going to understand the material.
I'd also add that you need mastery of several basic calculus concepts to do MA-level micro in any meaningful way. Optimization theory is all about solving Lagrangians, which requires something beyond "middle school algebra."
You will likely struggle mightily with a rigorous MPP program if you haven't studied math since 10th grade.
I'd suggest taking Calculus I-II, Linear Algebra, and Intro to Stats at your local community college. And you might need to take some pre-reqs to be able to take those courses if you left off at Geometry or Algebra II.
It's pretty expensive, but my Lenovo ThinkPad P1 Gen6 is fantastic.
But really if you need something more powerful than what you can get for ~$1000, then you should be utilizing a cloud solution.
There is substantially lower probability of legal complications if he voluntarily separates. The fact that he's just returning from workers comp increases the likelihood of him trying to shake you down.
I can't imagine that two weeks pay is a lot of money in the grand scheme of things. I'd just put up with him for two more weeks unless he does something fireable before his last day.
Like others have said, defer to HR. But I wouldn't push to terminate him.
I'd do biweekly 1:1s for 30 minutes. That works out to 4 hours per week for you.
You'd probably find this sub helpful: /r/dataanalysiscareers
First, the policy academic job market very, very competitive, especially if your demographic groups are historically overrepresented in the academy. And, frankly, the life of an assistant professor is not very enjoyable even if you love research. Factor in the extreme opportunity cost of a doctoral program (i.e., loss of 5-6 prime earning years) and it's a losing proposition, IMHO. Note: post docs in the field seldom lead to a subsequent academic position.
One of the challenging things about academic policy jobs is that it's not just PhD Public Administration/Policy programs that try to place in policy schools. A lot of PhD Economics applicants who do applied micro are as attractive to policy schools as PhD Public Administration/Policy. And you have some policy fields that have to contend with specialized programs (e.g., PhD Educational Policy from top Colleges of Education).
Higher education is currently contracting: fewer international students and fewer domestic students because of declining cohort sizes as well as fewer within the cohort choosing a 4 year degree. Many graduate programs are also experiencing contraction (although you wouldn't know if from this sub). Student credit hours are the currency of the realm in higher education and there's fewer of them to spread around. Because Schools of Public Policy are not as competitive for external research grants as other fields, budgets are super tight.
So the irony is that while it's easier now to get into a PhD Public Administration/Policy program than it was 5-10 years ago, it's much, much harder to get an academic job.
My advice for anyone is to get an elite MPP and then go into the workforce. IMHO, a PhD Public Administration/Policy is a losing proposition because there are very few jobs outside academia that require a PhD versus a MPP+4 years of experience. If you want to try to get into academia, do a PhD Economics and focus on applied micro that intersects with policy. You'll be as competitive an academic candidate and a PhD Economics will open many more doors for you outside academia.
Whatever else may be going on, they need to examine their hiring process if they're winding up with this many lemons.
At a high-level, there are two dimensions on which I evaluate team performance: deliverable quality and operational efficiency.
Deliverable quality is often in the eyes of the end-user and can be difficult to quantify absent surveys. Although we can assess whether the code is up to our standards as well as often run diagnostics and robustness checks on our models. We have a fairly rigorous QA protocol that minimizes chance for data errors.
There are various ways to measure operational efficiency. Were deadlines met? Did the project require expect effort? If you use Agile project management, then there are things like burndown charts that you can utilize to monitor progress.
If you're constantly going over budget in terms of effort (i.e., hours), then re-examine your assumptions about how long tasks should take. It could be that expectations are misaligned with reality. If that's not the issue, then identify what the obstacles are. Is it a data architecture issue? Data quality? Skills gaps?
When we go over budget in terms of effort on projects, half the time it used to be because I underestimated the complexities of data cleaning. My instinct was to envision ideal conditions, so I adjusted my expectations. We also improved our processes and worked with our data architect/engineer to structure the data in ways that were easier for end-users. So now our estimates for effort are closer to reality.
Finally, we do retrospective memos at the conclusion of each project to document what we learned. I highly recommend it. Basically it's a memo to our past selves about what we wished we had known before we started work on the project. This helps us not repeat the same mistakes as past projects.
My reports are all union, so virtually impossible. I got rid of a low performer by convincing him that a lateral move to another department was a promotion of sorts (small increase in pay), so he's someone else's problem now. Prior to him taking that position, I found some external job postings that would have represented a pay increase. I probably spent 2-4 hours a week for a couple months trying to find something better for him. And I would have given him a glowing recommendation.
No, you can't do exactly what you're suggesting.
But I think the way to leverage Azure DevOps for your use case is to clone a base repo that loads everything. That is, at the start of the script, load all the packages that you utilize (e.g., tidyverse, ggplot2, etc.). When someone starts using a new package, just have them update the base repo.
In terms of updating R, RStudio, packages, etc., build into the base repo the commands to search for updates and install when the script launches. 99% of changes made should be backwards compatible. But if you need an older version of something to run legacy code, amend that copy of the repo to rollback.
First step is to define your mission, core values, and strategic objectives. Make it as narrow and specific as possible. You're going to need to avoid "mission creep."
Second, figure out a revenue model for the next 3-5 years. Be realistic: how much can you collect from individual contributions, corporate donations, foundation grants, government contracts, etc. Be creative
Once you have your budget constraint, review every program carefully. Is it core to your mission? Are other entities capable of filling the gaps? Be ruthless and do not let the expertise of individual staff determine your programming. Make sure that you have sufficient unrestricted revenue to meet operational expenses--this is the Achilles' heal of many small nonprofits. Ask yourself two questions as you evaluate programs: is it scalable and is it sustainable?
Next, come up with a staffing model that will satisfy your budget constraint and programming needs. Again, do not let personal circumstances play into this. Cut positions where you must, pay only what you can afford. Be selective and strategic with your use of volunteers. While they do provide free labor, the opportunity cost of coordinating and supervising is non-trivial.
Finally, if you haven't already, formalize all this into a 5-year strategic plan. It's going to be built on assumptions, some of which will prove to be faulty. Figure out a fund balance that will allow you to survive short-term disruptions to your revenues. Ideally 6-9 months worth of expenses, but start out with just 3.
Managing a nonprofit is often identifying the least bad option to fulfill the mission while sustaining the viability of the organization. You're not going to be able to do everything you want, you're not going to be able to pay people as much as you may want, etc. It's a series of compromises.
In its heyday, Detroit had a different mix of blue collar vs white collar workers than many other cities. You need skyscrapers for white collar, but not so much for blue collar.
I'd say the ideal number of direct reports is 5-8.
FWIW, there was an HBR article about this that says 6:
I’m leaning toward giving the internal promotion a shot (if he’s willing) on an interim basis (5-6 months) while evaluating his performance.
Sounds prudent. Trust your instincts.
Document everything and be patient. If HR says that they need to build a case over time, then that's what you have to do.
If you have to ask question, you already know the answer.
You might find an OKR framework (Objectives and Key Results) helpful for aligning your priorities to a strategic plan.
First step is to identify which of the strategic goals you're hoping to advance. It's okay if some of your organizations objectives don't intersect with your work lanes. Once you've defined your objectives, map those onto a set of 3-5 objectives. Then for each Objective, come up with Key Results, which is basically how you'll you quantify success (think SMART goals).
If you do it well, you'll be sure to align most of your work activities with something that directly advances a strategic goal of the organization. And you'll have measurable outcomes for progress monitoring.
You'll want to periodically review OKRs (I'd say every 3-6 months) to make sure that they still reflect what you actually allocate time and other resources towards. If you have a major body of work that you can't map onto the strategic plan, then have a conversation with your manager about whether you're misaligned or if the strategic plan should be revised.
In an ideal world, every team has OKRs that align with the strategic plan.
Strategic Planning
nonprofit