Role Responsibilities and Success Criteria Questions

In short: A Business Intelligence (BI) team owns the dashboards leadership looks at, the ETL (extract, transform, load: the pipeline that moves raw data into reporting tables) jobs that populate them, and the definitions behind key metrics. Before a model goes to production, you need to align with BI so your model's output doesn't quietly contradict what a dashboard already shows.

Typical responsibilities and workflow

• Build and maintain dashboards (in tools like Looker or Tableau) that stakeholders check regularly.
• Own the ETL jobs and warehouse tables that feed those dashboards.
• Define and govern metric definitions (for example, deciding exactly what counts as an "active user").
• Run a request queue or self-serve semantic layer (a layer that maps raw warehouse tables to business-friendly names and definitions so non-engineers can query them directly) for ad hoc stakeholder questions, usually with a change-management process so a metric's definition can't silently shift mid-quarter.

Questions to ask before deploying a model

• "Where does my model's output need to land: a new table, a new column on an existing fact table (a central warehouse table storing the core measurable events, like orders or sign-ups, that dashboards aggregate), or a new dashboard tile, and who owns keeping the semantic layer in sync once it's there?"
• "Is there already a metric with a similar name my model's score could be confused with, like a rules-based risk score already on a dashboard?"
• "What's your process for backfilling or correcting history if my model's definition changes later?"

Worked example (data artifacts to request): ask BI for the canonical view that feeds the churn dashboard (so you train on the same source of truth executives see, not a private extract), the SQL behind the existing dashboard tile that resembles your target, and the entry for "active user" in the metrics dictionary if one exists, so you can cite exactly which version of a metric your model consumed.

Trade-offs and pitfalls: the real risk is BI and a new model producing two different numbers for "at-risk customers" that reach the same VP in the same week, quietly eroding trust in both. Version your model's output distinctly from an existing KPI, and be explicit that a model score is a prediction, not a redefinition of a metric BI already owns.

In short: Research and production optimize for different things, novelty and offline accuracy versus robustness and a stable serving budget, so the handoff needs an explicit, ongoing agreement rather than a one-time conversation.

Five expectations to set with a research team

1. Reproducibility: agree on a pinned random seed, a locked dependency and environment spec (a container image or requirements file, not "works on my laptop"), and the exact training data snapshot used, so a production retrain can be checked against the original research result.
2. Experiment logging: agree that any run considered "the" result gets logged in a tracked experiment system, for example MLflow or a similar tool that records parameters, metrics, and artifacts per run, rather than living in a personal notebook, so production can trace which exact run is being productionized.
3. Retraining cadence: agree explicitly on how often the model needs refreshing in production, for example weekly or triggered by detected data drift (a gradual shift in incoming data's statistical properties away from what the model was trained on), since a model trained once in a research notebook is not automatically a production retraining pipeline.
4. Inference constraints: set explicit latency, memory, and hardware budgets, for example a stated response-time limit on a single GPU, before research finalizes an architecture, since a model optimized purely for offline accuracy can be too large or slow to serve.
5. Performance versus compute trade-offs: agree on how much offline accuracy can be traded for meeting the latency budget, as a negotiated decision, not a surprise discovered during productionization.

Worked example: a research model hits its best offline metric using a large ensemble that would run roughly five times over the agreed inference latency budget. The concrete resolution is to negotiate down to a single distilled model (a smaller model trained to mimic the larger one's behavior), accepting a modest drop in offline accuracy in exchange for meeting the latency constraint, and writing that trade-off down explicitly so it isn't relitigated every release.

Trade-offs and pitfalls: the most common failure is treating this as a one-time handoff meeting rather than a standing agreement, since research keeps iterating on the model after the first productionization, and each new iteration can quietly reintroduce the same reproducibility or latency problem the first handoff solved.

In short: A Customer Success (CS) team owns the relationship with existing customers after the sale, focused on keeping them and growing them rather than winning new ones. Before you build anything that touches retention, you need to understand what CS already measures, what tools they trust, and what a false alert costs them in their day.

What CS actually does day to day

• Triages support escalations and account issues that come in through a ticketing tool.
• Runs quarterly business reviews (QBRs) with important accounts to check in on value delivered.
• Tracks renewal dates and reaches out proactively to accounts flagged as "at risk."
• Reports upward on retention and expansion numbers.

Primary objectives: reduce churn (customers who cancel or fail to renew), grow net revenue from existing accounts through upsell and cross-sell, and keep satisfaction high enough that renewal conversations aren't a fight.

Common tools: Zendesk (a support ticketing platform where issues are logged and resolved) and Gainsight (a customer-success platform that scores account health and automates outreach playbooks based on that score).

KPIs (key performance indicators, the numbers a team is measured against): Net Promoter Score (NPS, a "how likely are you to recommend us" survey rolled up into a single score), churn rate (the percentage of customers or revenue lost in a period), and Customer Satisfaction score (CSAT, a per-interaction "how satisfied were you" rating).

Stakeholder interactions: CS usually reports to a VP of Customer Success, coordinates renewals with Sales, hands off product feedback to Product, and escalates technical issues to Support or Engineering.

Three questions to ask CS before starting a retention project:

1. "How do you define churn today: a hard cancellation, a non-renewal at contract end, or a usage-based inactivity threshold?" A model trained against the wrong definition optimizes for the wrong outcome.
2. "What does Gainsight's existing health score get right and wrong, and does your team still trust its alerts?" If CS has already learned to ignore the incumbent score, that trust gap is exactly what a new model has to fix, not repeat.
3. "What's an acceptable false-positive rate for an at-risk flag, given that every flagged account costs your team an outreach call?" This turns a modeling metric into a cost CS actually feels.

Trade-offs and pitfalls: the common failure is shipping a technically strong churn model that CS never uses, because it scores accounts on a cadence or granularity the team can't act on (daily scores when CS only reviews monthly), or because it lives outside Gainsight instead of inside the tool they already work in. A retention model is only as useful as the workflow it plugs into.

In short: Before requesting access to a user dataset, a Machine Learning Engineer (MLE) needs to know not just whether the data exists, but whether it was ever cleared for the specific thing they want to build, because a technically strong model built on data used outside its consented purpose is a compliance failure, not an edge case.

Key questions to ask Legal or Privacy, and how each could change the approach

1. "What was this data's stated purpose when the user consented to it, and does building a retention model fall inside that purpose (purpose limitation)?" This could mean excluding fields that were collected for an unrelated purpose, for example support-ticket free text collected only to resolve a complaint, even if it turns out to be predictive.
2. "Which fields count as personally identifiable information (PII) here, and can they be pseudonymized (swapped for an artificial identifier that hides the real identity from you but stays reversible by someone holding the key) or hashed before I ever see them?" This could push feature engineering upstream onto hashed identifiers, or require dropping free-text or precise location fields that are hard to fully anonymize.
3. "What are the retention and deletion requirements, for example must a user's data be deletable within a stated number of days of their request, and how does that interact with a model that may have implicitly memorized details from training?" This could push the training approach toward a rolling window of recent data rather than an ever-growing historical store, plus a defined plan for what happens to the model if someone in its training set later requests deletion.
4. "Is there a use restriction I need to encode as a hard constraint, for example a rule that this data cannot be used to influence pricing, or that certain fields correlated with a protected class (a legally protected group, such as race, gender, or age, that anti-discrimination law forbids using as a basis for decisions) cannot be used?" This could mean excluding specific features outright, or applying a fairness constraint at training time, rather than discovering the restriction after the model has already shipped.

Worked example: Legal confirms that location data was collected only for fraud detection, not for retention modeling. Even though location is predictive of churn, the MLE drops those fields and instead engineers proxy features from data explicitly cleared for the retention use case, such as in-product usage patterns collected under a broader analytics consent.

Trade-offs and pitfalls: the model will likely lose some predictive power by respecting these constraints. That's the correct trade to make: a marginally weaker model trained on properly scoped data is a solvable problem, while a strong model trained outside its consented purpose is a trust and legal exposure that no amount of accuracy fixes.

On an AI-driven feature, the Product Manager (PM) owns what problem is worth solving and how "good enough" will be judged; the engineer owns whether and how it's technically achievable. Those two decisions have to happen together, because AI features rarely have a fixed, guaranteed accuracy ceiling the way a normal feature has a fixed, guaranteed scope.

The PM's decision areas

• Roadmap: sequencing this AI feature against everything else competing for the team's time.
• Success metrics: defining the target business or product metric and the model-quality bar that counts as "good enough," rather than an open-ended "make it more accurate."
• Prioritization: deciding whether a simpler rule-based fallback ships first while the model matures, or whether the feature has to be model-based from day one.
• Stakeholder alignment: managing expectations with sales and leadership about timelines and failure modes, since machine learning (ML) work often needs iteration cycles that a fixed launch date doesn't naturally accommodate.

Three questions I'd ask at kickoff

1. "What's the minimum quality bar for this to ship, and what happens to the user when the model is wrong?" An AI feature's failure mode differs from a normal bug; being wrong sometimes can be an acceptable trade-off, but only if everyone agreed on that upfront.
2. "Is there a non-ML fallback we ship first, or does this need to be model-based from day one?" This single answer changes the realistic timeline dramatically.
3. "How will we know after launch whether this is actually working, and who owns watching that?" Aligns on the monitoring and evaluation plan before launch, not as an afterthought once something looks off.

Worked example

For a proposed "smart reply" suggestion feature, the PM and I agree upfront that the acceptable error mode is a suggestion the user simply ignores, not one that sends anything automatically, which lets us ship a lower-precision first version safely and iterate based on real usage rather than blocking launch on a higher accuracy bar.

Trade-offs and pitfalls

Skipping these questions leaves engineers chasing an undefined "make it better" indefinitely, since there's no PM-defined stopping point telling you when the feature is actually done.