Flowt: Data Analyst

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    "description": "<p><span style=\"font-size: 14pt; font-weight: bold\">About Flowt</span></p>\n<p><span style=\"font-size: 10pt\">Flowt is building a financial intelligence platform to unlock access to financing for climate-smart small and growing businesses (SGBs) in Africa. We help SGBs become investor-ready by transforming bank and M-Pesa statements into cash-based profit &amp; loss and cashflow statements and generating financial insights, then lending on the basis of this data.</span></p>\n<p><span style=\"font-size: 10pt\">Our platform combines:</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Structured onboarding and financial data capture</span></li>\n<li><span style=\"font-size: 10pt\">Integrations with bookkeeping and banking systems</span></li>\n<li><span style=\"font-size: 10pt\">Workflow automation for financing products such as invoice financing, purchase order financing, inventory financing, and asset financing</span></li>\n<li><span style=\"font-size: 10pt\">AI-assisted validation, underwriting, and scoring systems to improve speed, accuracy, consistency, and fairness in credit decisions</span></li>\n</ul>\n<p><span style=\"font-size: 10pt\">We work closely with lenders and financing partners to help businesses access capital faster and more reliably.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 14pt; font-weight: bold\">About the Role</span></p>\n<p><span style=\"font-size: 10pt\">We are hiring a Data Analyst to sit at the centre of Flowt's data and AI ecosystem — the person whose judgment and attention to detail keeps every layer of the platform honest. As Flowt ingests financial data from multiple ERP and accounting systems, processes financial documents through AI extraction pipelines, calculates complex credit and financial metrics, and runs internal models to support credit decisions, this role ensures that what comes out of those systems is correct, complete, and trustworthy.</span></p>\n<p><span style=\"font-size: 10pt\">This is a human-in-the-loop role in the most meaningful sense: you are not just reviewing outputs — you are the feedback loop that makes the system smarter over time. Your corrections improve AI model accuracy. Your observations identify gaps in financial data quality. Your analysis of metric outputs flags formula errors before they reach a lender. You will work closely with the engineering, AI, and operations teams, translating what you see in the data into concrete improvements to the platform.</span></p>\n<p><span style=\"font-size: 10pt\">This role suits someone with a strong analytical foundation, genuine curiosity about financial data, and the discipline to be rigorous even when working under time pressure. You will handle real financial data for real businesses, and accuracy here has real consequences.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 14pt; font-weight: bold\">What You'll Own</span></p>\n<p><span style=\"font-size: 12pt; font-weight: bold\">1) ERP &amp; Accounting Data Review</span></p>\n<p><span style=\"font-size: 10pt\">Flowt pulls financial data from multiple ERP and accounting platforms on behalf of borrowers. Your job is to ensure that what arrives is complete, correctly mapped, and ready to use:</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Review financial data synced from ERP and accounting integrations (Zoho Books, Xero, Odoo, QuickBooks, and others) for completeness, structural integrity, and logical consistency</span></li>\n<li><span style=\"font-size: 10pt\">Validate that chart-of-accounts mapping has been applied correctly: that income, expense, asset, and liability accounts have been classified into the right categories and that unusual or uncategorised accounts are flagged for review</span></li>\n<li><span style=\"font-size: 10pt\">Identify data quality issues in ERP-sourced records: duplicate transactions, missing periods, mismatched currencies, negative balances in unexpected accounts, and incomplete sync histories</span></li>\n<li><span style=\"font-size: 10pt\">Cross-reference ERP data against other submitted financial documents (bank statements, audited accounts) to detect discrepancies — revenue figures that do not reconcile, expense categories that appear inconsistent, or balances that do not agree across sources</span></li>\n<li><span style=\"font-size: 10pt\">Document recurring data quality patterns by ERP source and feed findings back to the engineering team to improve mapping rules, validation logic, and sync monitoring</span></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">2) Financial Metrics Validation</span></p>\n<p><span style=\"font-size: 10pt\">Flowt calculates a structured set of financial metrics from ingested data to assess credit readiness. You are responsible for ensuring those metrics are accurate and meaningful:</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Review calculated financial metrics for logical consistency and correctness: revenue trends, gross margin, EBITDA, operating cash flow, cash conversion cycle, burn rate, runway, inventory turnover, and receivables ageing, among others</span></li>\n<li><span style=\"font-size: 10pt\">Validate metric outputs against the underlying source data — tracing a calculated figure back through the formula to the raw inputs and identifying where errors have been introduced</span></li>\n<li><span style=\"font-size: 10pt\">Flag metrics that appear anomalous: values that fall outside expected ranges for the business type, sector, or size; trends that contradict other signals in the same dataset; or outputs that suggest a formula has been applied to incorrect or incomplete data</span></li>\n<li><span style=\"font-size: 10pt\">Review multi-currency metric calculations to ensure that FX rate application is consistent, that the correct rate dates have been used, and that the disclosed assumptions match the actual calculation</span></li>\n<li><span style=\"font-size: 10pt\">Maintain a live log of metric errors and edge cases observed in production, structured in a way that the engineering team can use to improve formula definitions, calculation versioning, and validation rules</span></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">3) Document Extraction Review &amp; AI Output Validation</span></p>\n<p><span style=\"font-size: 10pt\">Flowt uses AI to extract structured data from financial documents — bank statements, audited accounts, invoices, purchase orders, tax certificates, and more. You are the human reviewer who ensures those extractions are correct before they are used:</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Review AI-extracted data from financial documents — field by field where necessary — comparing outputs against the source document to identify errors, omissions, hallucinations, and formatting failures</span></li>\n<li><span style=\"font-size: 10pt\">Assess confidence scores and extraction quality across document types: identify patterns in where the AI performs well, where it struggles, and what document characteristics (quality, layout, language, format) drive poor performance</span></li>\n<li><span style=\"font-size: 10pt\">Provide structured corrections through the validation queue: not just accepting or rejecting outputs, but annotating specific fields with the correct value and a reason code so that corrections feed directly into model improvement</span></li>\n<li><span style=\"font-size: 10pt\">Review extracted financial tables from bank statements and audited accounts for structural completeness: correct column headers, properly split multi-period data, correctly identified opening and closing balances, and accurate transaction categorisation</span></li>\n<li><span style=\"font-size: 10pt\">Flag systemic extraction failures — recurring errors on a specific document type, bank, or format — and escalate them to the AI team with enough structured detail to drive targeted model improvements</span></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">4) AI Model Performance Monitoring</span></p>\n<p><span style=\"font-size: 10pt\">As Flowt develops and deploys internal AI and ML models, this role tracks how well those models are performing against real-world data — and raises the alarm when performance degrades:</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Monitor extraction accuracy metrics across Flowt's document intelligence models on an ongoing basis: track precision, recall, and field-level accuracy by document type, data source, and model version over time</span></li>\n<li><span style=\"font-size: 10pt\">Maintain the model performance dashboard: accuracy trends, error rate by category, volume processed, human correction rate, and the delta between model versions after each update</span></li>\n<li><span style=\"font-size: 10pt\">Identify model drift: patterns where a model that previously performed well begins producing more errors — whether due to new document formats, new borrower types, data distribution shifts, or changes in input quality</span></li>\n<li><span style=\"font-size: 10pt\">Review the output of transaction categorisation models: assess whether income and expense categories are being assigned correctly across different business types, flag systematic miscategorisations, and provide corrected labels with supporting rationale</span></li>\n<li><span style=\"font-size: 10pt\">Analyse anomaly detection outputs: review flagged transactions, unusual financial patterns, and risk signals to assess whether the model is flagging genuine issues or generating noise — and feed that signal back into the model improvement cycle</span></li>\n<li><span style=\"font-size: 10pt\">Contribute to model evaluation benchmarks: help define what \"correct\" looks like for new document types or new metric calculations, build labelled test sets from reviewed cases, and participate in structured evaluation of new model versions before they are promoted to production</span></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">5) Data Quality Operations &amp; Continuous Improvement</span></p>\n<p><span style=\"font-size: 10pt\">Beyond individual case reviews, this role drives systemic improvements to the quality of data flowing through Flowt's platform:</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Maintain a structured issue log of data quality failures, extraction errors, metric anomalies, and model performance gaps — categorised, prioritised, and linked to the business impact of each issue type</span></li>\n<li><span style=\"font-size: 10pt\">Produce regular data quality reports for the product and engineering teams: error volumes by category, resolution rates, time-to-resolution, and trend analysis showing whether quality is improving or deteriorating</span></li>\n<li><span style=\"font-size: 10pt\">Identify and document recurring data quality patterns that should be addressed with automated validation rules rather than ongoing human review — and work with engineering to implement them</span></li>\n<li><span style=\"font-size: 10pt\">Review the completeness and consistency of data entering the credit assessment process: ensure that every application reaching a lender has been through appropriate data review, and that gaps or anomalies are disclosed and resolved before submission</span></li>\n<li><span style=\"font-size: 10pt\">Contribute to the development of internal data quality standards, validation checklists, and review protocols that can be used to onboard and train future team members</span></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">6) Analyst Support &amp; Operational Review</span></p>\n<p><span style=\"font-size: 10pt\">This role also supports the broader operations team with data analysis and structured financial review:</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Support the credit and operations team with ad hoc financial data analysis: pulling, cleaning, and interpreting data to answer specific questions about a borrower's financial position</span></li>\n<li><span style=\"font-size: 10pt\">Assist in preparing borrower financial profiles for analyst and lender review: summarising key metrics, flagging data quality concerns, and ensuring that the financial picture presented is accurate and complete</span></li>\n<li><span style=\"font-size: 10pt\">Review submitted financial documents against Flowt's document standards: correct format, correct period, matching entity details, and compliance with product-specific document requirements</span></li>\n<li><span style=\"font-size: 10pt\">Contribute to internal reporting: portfolio data quality dashboards, borrower data completeness rates, and operational metrics that help the team understand the health of the data flowing through the platform</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-size: 14pt; font-weight: bold\">What Success Looks Like (First 90 Days)</span></p>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Weeks 1–2: Learn the Data</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Develop a working understanding of Flowt's data flows: how ERP data is ingested and mapped, how financial documents are processed, how metrics are calculated, and how AI outputs are generated and stored</span></li>\n<li><span style=\"font-size: 10pt\">Complete supervised reviews of ERP data sets, extracted documents, and calculated metrics across each major financial document type</span></li>\n<li><span style=\"font-size: 10pt\">Learn the validation queue: how to work through AI output reviews, how to structure corrections, and how findings are fed back into the model improvement process</span></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Weeks 3–6: Independent Review &amp; Pattern Recognition</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Independently validate ERP data, financial metrics, and document extractions within agreed turnaround times and accuracy standards</span></li>\n<li><span style=\"font-size: 10pt\">Identify and document at least three recurring data quality or model performance patterns — with enough structured detail for the engineering or AI team to act on</span></li>\n<li><span style=\"font-size: 10pt\">Begin contributing to the model performance log: tracking accuracy trends, flagging degradation, and annotating edge cases with corrected labels</span></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Weeks 7–12: Systemic Contribution</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Produce the first structured data quality report covering error volumes, trends, and recommended interventions</span></li>\n<li><span style=\"font-size: 10pt\">Contribute at least one improvement to the validation process: a new validation rule, a refined review checklist, or a structured escalation protocol for a specific error type</span></li>\n<li><span style=\"font-size: 10pt\">Demonstrate that your corrections are making a measurable difference: model accuracy in your reviewed categories improving, or recurring issues that you surfaced being resolved in the platform</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-size: 14pt; font-weight: bold\">Required Qualifications</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Experience in data analysis, ideally with exposure to financial data </span></li>\n<li><span style=\"font-size: 10pt\">Strong understanding of financial statements: profit and loss, cash flow statement, balance sheet, and the relationships between them</span></li>\n<li><span style=\"font-size: 10pt\">Comfortable working with structured data in spreadsheets and SQL: filtering, aggregating, cross-referencing, and spotting anomalies in large data sets</span></li>\n<li><span style=\"font-size: 10pt\">High attention to detail: able to identify a misclassified account entry, a wrong sign in a cash flow calculation, or an AI-extracted value that does not match the source document</span></li>\n<li><span style=\"font-size: 10pt\">Analytical rigour: able to distinguish between a data error and a business reality, and to communicate findings precisely without overstating or understating the issue</span></li>\n<li><span style=\"font-size: 10pt\">Clear written communication: comfortable documenting findings, writing error reports, and providing structured feedback that engineers and AI teams can act on</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-size: 14pt; font-weight: bold\">Nice to Have</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Experience working with accounting or ERP systems (Xero, QuickBooks, Zoho Books, Odoo, Sage) — either as a user, analyst, or integrator</span></li>\n<li><span style=\"font-size: 10pt\">Familiarity with AI or ML output review: understanding of what hallucinations, confidence scores, and extraction errors look like in practice</span></li>\n<li><span style=\"font-size: 10pt\">Experience in credit analysis, lending, or financial due diligence — particularly with SME financials</span></li>\n<li><span style=\"font-size: 10pt\">CPA, ACCA, or equivalent accounting qualification, or active pursuit of one</span></li>\n<li><span style=\"font-size: 10pt\">Exposure to data quality frameworks, validation rule design, or structured QA processes</span></li>\n<li><span style=\"font-size: 10pt\">Ability to write basic SQL queries for data investigation and anomaly detection</span></li>\n<li><span style=\"font-size: 10pt\">Experience with data visualisation tools (Metabase, Tableau, Google Data Studio, or similar) for building operational dashboards</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-size: 14pt; font-weight: bold\">Our Working Style</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">Detail-first: in financial systems, a small error in a source metric can cascade into a wrong credit decision — accuracy is the job, not a nice-to-have</span></li>\n<li><span style=\"font-size: 10pt\">Feedback-driven: your corrections and observations are not just operational — they make the platform smarter; we treat every structured piece of feedback as an engineering input</span></li>\n<li><span style=\"font-size: 10pt\">Collaborative across disciplines: you will work with engineers, AI developers, and operations staff — translating between data realities and technical solutions is a core part of the role</span></li>\n<li><span style=\"font-size: 10pt\">Continuous improvement: we expect you to notice patterns, propose fixes, and help build the validation infrastructure that reduces manual review over time</span></li>\n<li><span style=\"font-size: 10pt\">Fast-paced but careful: we move quickly as a team, but we do not trade accuracy for speed in financial data — if something is wrong, it gets flagged, not waved through</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-size: 14pt; font-weight: bold\">Compensation &amp; Benefits</span></p>\n<ul>\n<li><span style=\"font-size: 10pt\">KES 30,000- KES 40,000 per month depending on experience</span></li>\n<li><span style=\"font-size: 10pt\">Clear growth path into senior data, operations, risk, or product roles as Flowt scales</span></li>\n<li><span style=\"font-size: 10pt\">Direct exposure to AI systems, financial data infrastructure, and the inner workings of a credit platform.</span></li>\n<li><span style=\"font-size: 10pt\">Hybrid work flexibility and a mission-driven environment</span></li>\n</ul>",
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