Data Driven Recommendations and Impact Questions

Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.

EasyTechnical

0 practiced

You're preparing an executive summary recommending a pilot for a personalization feature. The pilot shows a modest lift but requires non-trivial engineering. Outline a 3-part one-page executive summary: (1) headline recommendation with one-number impact and cost estimate, (2) key assumptions and risks, (3) proposed next steps and success criteria for a 3-month pilot.

EasyTechnical

0 practiced

Define Average Treatment Effect (ATE) and Average Treatment effect on the Treated (ATT). In an experiment where only a subset of invited users actually receive the treatment, which estimand (ATE or ATT) would you report and why?

HardTechnical

0 practiced

You manage a generative recommender that can produce sensitive or harmful suggestions. Propose a measurement and mitigation plan to quantify and reduce risk. Include automated detection, human-in-the-loop review rates, adversarial testing, user reporting metrics, and an incident response playbook.

HardTechnical

0 practiced

Implement a doubly robust estimator for Average Treatment Effect in Python. Input is a Pandas DataFrame with columns: user_id, treatment (0/1), outcome (numeric), and covariates (x1, x2, ...). Provide code to fit a propensity model and outcome regression, compute the doubly robust ATE, and discuss how you would regularize and validate both models.

EasyTechnical

0 practiced

Describe a concise dashboard (list of visualizations and KPIs) to monitor adoption and downstream outcomes after releasing a recommender. Include at least five visual elements (e.g., time-series, cohort grid), their purpose, and how you'd set alert thresholds.

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