Can you share a situation where you took a risk and it didn't turn out as planned?

Question

Anonymous · Accepted Answer

Situation:
At my previous role, I was working on a churn prediction model for a food
delivery subscription program. The goal was to proactively identify users likely
to churn and design interventions to retain them. The safe route would have been
to use a traditional logistic regression model with standard RFM (Recency,
Frequency, Monetary) features, which we knew would give us “good enough”
performance.
Task:
I believed we could significantly improve prediction accuracy—and ultimately
retention—by incorporating session-level behavioral data using a more complex
machine learning approach. But doing this came with risk: more complexity meant
slower iteration, possible model interpretability concerns, and engineering
resources for new feature pipelines.
Action:
I laid out the trade-offs to my team and stakeholders during a planning session.
We held a working session with our product manager, engineering lead, and
another DS to decide how much to invest upfront. I proposed a two-track
strategy:
 * In the short term, we'd ship a simple logistic regression using existing data
   to get a baseline.
 * In parallel, I built a prototype using XGBoost with behavioral features like
   time of day ordering patterns, days since last session, and order scroll
   depth.

Throughout, I regularly shared updates with the team in Slack and in weekly
check-ins. I also set up interpretable SHAP analysis so the model wouldn’t be a
black box to marketing and product partners.
Result:
The baseline model gave us a lift in retention campaign targeting—about a 5%
improvement in retention over random targeting. But the more complex model
improved that by an additional 9 percentage points. After a successful A/B test,
the marketing team adopted it for ongoing campaigns. Our engineering team later
prioritized automating the behavioral feature pipeline due to the business
impact.
Why it was a calculated risk:
We risked sunk time and complexity, but mitigated it with parallel development,
stakeholder involvement, and clear success criteria. The team was involved
throughout because buy-in and trust were essential for implementation—and I
wanted to ensure everyone understood the “why,” not just the “what.”

Anonymous · Answer

Situation:
When we were preparing the launch of Cisco Webex Interact in the UK market, we
faced a key decision: whether to completely redesign the outbound messaging
architecture into microservices with asynchronous processing, or iterate on the
existing monolith. The safer choice was incremental updates, but it would limit
our scalability and future growth.
Task:
As the engineering manager, my responsibility was to ensure we could handle
projected customer growth, deliver a stable platform, and minimize long-term
technical debt — while balancing business timelines and risk.
Action:
I proposed moving forward with a microservices-based architecture leveraging
RabbitMQ, Redis, and AWS EKS. Before committing, I involved my senior engineers,
architects, and product stakeholders in technical design sessions. We mapped out
potential risks like message loss, operational complexity, and deployment
challenges.
Result:
The calculated risk paid off. We scaled outbound messaging 6x, achieved 99.99%
uptime, and eliminated many of the bottlenecks that would have limited us later.
The team’s early involvement not only ensured technical robustness, but also
created strong ownership and confidence during go-live. This architectural shift
became a key enabler for Webex Interact’s success in new markets.

Can you share a situation where you took a risk and it didn't turn out as planned?

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