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
2. 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
3. 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
4. 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
5. 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
6. 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:
•
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

•
are strong in sql — joins, window functions, aggregations, and validating messy real-world datasets with confidence

•
write python — not just basic scripts; able to automate reports, build data pipelines, and work with apis

•
have hands-on experience with tableau or equivalent bi tools for building dashboards stakeholders actually use

•
have done funnel, cohort, and experiment analysis independently and can walk through trade-offs and limitations clearly

•
can work with ambiguous, incomplete, or inconsistent data — you sanity-check, flag anomalies, and build trust in your numbers

•
communicate structured insights to non-technical stakeholders with clarity and conviction

•
have strong business acumen — you understand levers for growth, profitability, and unit economics

Department: Kuvera
Team: corporate & business strategy