business analytics

1. business reviews & reporting cadence

• own weekly, bi-weekly, and monthly business review decks end-to-end: pull data, structure narrative, present to leadership
• set up and maintain kpi dashboards, product funnels, capex trackers, and north-star metric frameworks from scratch
• be the single source of truth for business performance — data that goes into leadership reviews and investor conversations originates with you

1. watchtower: anomaly detection & early warning

• build proactive monitoring systems that flag drops in aum inflows, sip health, cross-sell conversion, funnel leakages, or user portfolio anomalies — before leadership asks
• track 25+ primary metrics cut by product, cohort, time, and user segment daily
• diagnose root causes, not just report numbers; propose and drive resolution

1. user & product analytics

• run funnel, cohort, and user behaviour analysis on the portfolio user base to surface cross-sell, engagement, and monetisation opportunities
• translate ambiguous business questions into structured analyses with clear recommendations — not just data outputs
• identify high-propensity user segments and work with product/growth teams to activate them
• support product decisions with data, including a/b experiments and feature adoption analysis

1. growth & monetisation strategy

• identify data-led opportunities to improve product cross-sell and business volumes across the wealth product suite
• build p&l and roi models to evaluate new product launches, campaigns, and partnerships
• support expansion into new products (global equities, and beyond) with analytical frameworks

1. investor relations data support

• prepare monthly investor update packs, ad-hoc data briefs on product performance, and materials required for fundraising conversations
• as the company moves toward ipo, ensure data integrity and narrative consistency across all external-facing numbers
• you won't have direct investor interaction initially, but your work is what leadership takes into those rooms

1. automation & ai-augmented analytics

• proactively automate recurring reports using python and ai/agentic frameworks — the bar is reducing manual bandwidth by at least 25%
• build pipelines that replace repetitive manual effort; comfort with llm-assisted workflows and agentic tools is a real expectation, not a nice-to-have
• champion a culture of "build once, run forever" for monitoring and reporting

you should apply if you

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• have 2–5 years of experience as an analyst at a high-growth fintech or tech startup, management consulting firm, pe/vc fund, or research analytics firm
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• are strong in sql — joins, window functions, aggregations, and validating messy real-world datasets with confidence
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• write python — not just basic scripts; able to automate reports, build data pipelines, and work with apis
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• have hands-on experience with tableau or equivalent bi tools for building dashboards stakeholders actually use
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• have done funnel, cohort, and experiment analysis independently and can walk through trade-offs and limitations clearly
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• can work with ambiguous, incomplete, or inconsistent data — you sanity-check, flag anomalies, and build trust in your numbers
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• communicate structured insights to non-technical stakeholders with clarity and conviction
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• have strong business acumen — you understand levers for growth, profitability, and unit economics