Multi-Access Edge Computing (MEC) is increasingly growing in prominence as the de facto enabler for ultra-low latency access to services. MEC averts the high network latencies often encountered in accessing cloud services by deploying application instances on edge servers situated near Internet-of-Things (IoT) device users. Workloads generated by IoT devices can then either be executed locally on the devices or offloaded to the MEC servers. A key cornerstone of the MEC environment is a service placement policy that determines the deployment of services on MEC servers. A service placement policy plays a critical role towards determining the trade-offs involved between latency experienced by users as a function of the resource contention and the resulting energy consumption. In this context, we propose a static-dynamic service placement policy for MEC. The static policy is geared towards placement of services in a prioritised order by leveraging Probabilistic Model Checking, a Formal Methods technique, to ensure probabilistic guarantees on the trade-offs between latencies and energy consumption of edge sites. The dynamic policy alters the static service allocation to cater to runtime variability in latency requirements. We present experimental results on a real-world service usage dataset to show the benefits of our approach over conventional approaches.