When a data scientist is asked to optimize an email campaign, it just means improving targeting, finding best time / day to send the message, improving segmentation of contact lists to get better metrics (opens, clicks, conversions, opens-to-clicks ratio, clicks-to-conversion ratio, revenue per lead etc.)
However this is only one aspect of email campaign optimization. Full optimization consists of
These three aspects are (unfortunately) always treated as separate silos handled by very different teams in any company (analytics team for #1, engineering / IT or vendor for #2, and product / marketing for #3). Here I discuss how some aspects of #2 can be handled by a data scientist.
Case study: how a data scientist reduced email bounce rate from 30% to 17%
High bounce rate typically occurs with imported lists. Improving open rate / bounce rate / unsubscribe rate is critical and should be the #1 priority. Without perfect deliverability metrics, your email ends up in junk boxes, unread, spam filters and may not be delivered at all by Gmail, Hotmail, Yahoo or some other providers, resulting in very poor performance no matter how great your text message is. Also, it will be very hard for you to grow your list.
Anyway, here's how we improved our bounce rate. A more complicated methodology (because it involves the two other silots) applies to unsubscribe and open rates. Our mailing list in question had about 1.4MM contacts.
Step A (test)
Test a message on the first 100K contacts (randomly selected from the 1.4MM list)
Step B (outside the test)
Note that by doing so, you will also rerroneously delete good contacts (false positives) from your mailing list. But by carefully choosing the algorithm threshold parameters (40% and 3 in this example), the benefits will far outweight the drawbacks.
Related article: 10+ Great Metrics and Strategies for Email Campaign Optimization