Describe building a professional relationship
Company: Bank of America
Role: Data Scientist
Category: Behavioral & Leadership
Difficulty: medium
Interview Round: Take-home Project
Describe a time when you actively attempted to develop a strong relationship with a teammate, manager or customer customer/client. In your response, please share the specific actions you took to build the relationship, any challenges you faced, how you addressed them, and what resulted.
Quick Answer: This question evaluates interpersonal communication, collaboration, stakeholder management, and leadership competencies through demonstrated relationship-building.
Solution
Approach this with the STAR method, tying your story to outcomes relevant to analytics/data science (clarity on goals, improved collaboration, measurable impact).
1) How to structure your answer (STAR+L)
- Situation: Briefly set the context (project, stakeholders, stakes, timeline).
- Task: Define your goal and the relationship gap you observed.
- Actions: Detail 3–5 specific, proactive actions you took to build trust and alignment.
- Result: Quantify impact (time saved, metrics improved, decisions enabled). Make it concrete.
- Learning: One sentence on what you’d repeat or do differently.
2) Action ideas that resonate for Data Scientists
- Stakeholder mapping: Identify decision-makers vs. influencers (e.g., product, risk, compliance, engineering).
- Goal alignment: Co-create success criteria and decision thresholds; translate technical metrics to business outcomes.
- Communication plan: Cadenced updates (weekly syncs, brief notes), shared artifacts (PRDs, dashboards, model cards).
- Transparency: Surface risks, assumptions, and trade-offs early; maintain an experiment log.
- Quick wins: Deliver small proofs or prototypes to show value and reliability.
- Empathy and language: Bridge jargon; invite feedback; clarify constraints (e.g., regulatory or data quality).
- Conflict resolution: Reframe disagreements around shared objectives and data.
3) Sample answer (tailor to your experience)
Situation: On a credit risk model refresh, I needed close partnership with our risk manager to get model validation and approval before quarter-end. Historically, DS and risk had tension due to limited transparency and late-stage surprises.
Task: Build a strong working relationship with the risk manager so we could align on requirements early, reduce review cycles, and meet the deadline without compromising governance.
Actions:
- Scheduled a 45-minute kickoff to co-define success: approved by validation, stable performance across segments, and clear monitoring thresholds (KS drift < 10%, population stability index < 0.1).
- Translated model metrics into business terms (e.g., how a 0.07 AUC lift affects approvals and loss rate) and created a one-page model card covering purpose, features, bias checks, and limitations.
- Set a weekly 20-minute check-in and a shared tracker with assumptions, data lineage, and open issues; invited early feedback on feature eligibility and documentation.
- Ran a small pilot on a 10% sample to quantify impact and safety: reduced manual reviews by 15% with no significant lift in delinquency; shared segment-level fairness and stability results.
- When concerns arose about one bureau-derived feature, I proposed an interpretable alternative and added a challenger/monitoring plan to revisit post-launch.
Result:
- Cut validation rework from two rounds to one, securing approval 3 weeks earlier than prior cycles.
- Improved AUC from 0.72 to 0.79, increasing approvals by ~5% at constant risk; reduced manual underwriting by 12% in the first month.
- Established an ongoing relationship: monthly governance syncs and an automated monitoring dashboard, which the risk team later adopted as a standard.
Learning: Proactive transparency plus shared artifacts (model card, tracker) builds trust faster than over-indexing on technical depth alone.
4) Template you can customize
- Situation: [Project, why it mattered, who the counterpart was].
- Task: [Your relationship goal and what needed to change].
- Actions:
1. [Alignment step: shared goals/criteria].
2. [Communication cadence and artifacts].
3. [Early value/quick win to de-risk].
4. [Addressed a specific concern with data or alternative].
- Result: [Time saved, approvals accelerated, metric improvements, adoption, smoother governance].
- Learning: [Repeatable principle you’ll carry forward].
5) Pitfalls and guardrails
- Don’t be vague—anchor actions in concrete behaviors and artifacts.
- Avoid blaming stakeholders; frame conflicts as misaligned incentives or information gaps.
- Quantify results when possible; if confidential, use relative terms (e.g., “reduced rework by ~30%”).
- Keep it 2–3 minutes; prioritize 1–2 impactful actions over a long list.
- For regulated contexts, emphasize documentation, monitoring, and alignment with governance.
6) Optional variations (if you lack a risk/governance example)
- Teammate: Onboarded a new engineer by pairing on data pipelines, creating runbooks, and reducing on-call incidents by 40%.
- Internal client: Partnered with marketing on uplift modeling; ran a controlled pilot, improved incremental conversions by 8%, and set up weekly insights reviews to maintain alignment.
If you prepare one strong story in this format and one backup, you can flex to teammate, manager, or client variants on the spot.