Data Scientist

window.notificationMessages = []; Find a Job + + + + Search const TopSearch = { handleSubmit: function (event) { event.preventDefault(); event.stopPropagation();

const form = event.target;

const getValue = (name) => { if (form[name]) { if (form[name].type) { if (["select-one", "text", "hidden"].indexOf(form[name].type) > -1) { return form[name].value; } if (["checkbox", "radio"].indexOf(form[name].type) > -1 && form[name].checked) { return form[name].value; } } else if (form[name].length) { return form[name].value; } }

return ""; };

const experienceLevel = getValue("experience[]") || getValue("experience"); const jobFunction = getValue("job_function[]") || getValue("job_function"); const industry = getValue("industry[]") || getValue("industry"); const location = getValue("location[]") || getValue("location"); const workType = getValue("work_type"); const term = getValue("q"); const sortBy = getValue("sort_by");

// If the logged in user is authenticated // Append authenticated value to make url unique/different to logged out url // same action is happening in SearchFilterViewComposer // line 589 in the multiSearchFilterHref function const authenticated = document.getElementById("authenticated").value; let toUrl = ""; if (industry.trim() && jobFunction.trim()) { const segments = [jobFunction, location, workType].filter((x) => x); toUrl = ${segments.join("/")}?industry=${industry}; } else { const segments = [jobFunction, industry, location, workType].filter((x) => x); toUrl = segments.join("/"); }

if (experienceLevel) { toUrl += ${toUrl.indexOf("?") === -1 ? "?" : "&"}experience=${experienceLevel}; }

if (term.trim()) { toUrl += ${toUrl.indexOf("?") === -1 ? "?" : "&"}q=${encodeURI(term.replace(" ", "+"))}; }

if (sortBy.trim()) { toUrl += ${toUrl.indexOf("?") === -1 ? "?" : "&"}sort=${sortBy}; }

if (authenticated === "true") { toUrl += ${toUrl.indexOf("?") === -1 ? "?" : "&"}authenticated=${Date.now()}; }

const wholeUrl = ${form.getAttribute("action")}/${toUrl.toLocaleLowerCase()}; window.location.assign(wholeUrl.replace(//$/, ""));

return false; }, }; Homepage Engineering & Technology IT & Telecoms Nairobi Contract Data Scientist Data Scientist Anonymous Employer Engineering & Technology 6 days ago Easy apply New Nairobi Contract IT & Telecoms Confidential Share link Share on WhatsApp Share on LinkedIn Share on Facebook Share on Twitter Share via SMS Job summary We are looking for a Data Scientist to play a critical role in driving the data intelligence layer of the implementation programme. Experience Level: Senior level Experience Length: 4 years Language Requirement: English Working Hours: Contract - 9 to 5 Applicant Location: Kenya Job descriptions & requirements 1. Role Title & Level Data Scientist Level: Senior (4-7+ years of relevant experience) 2. Engagement Summary

• Engagement Type: Contract / Secondment
• Squad Context: Embedded within the Visa–client joint Tech Squad; leads all data science, analytics, and measurement workstreams supporting digital acquisition, activation, and usage initiatives
• Expected Duration: [12 months]
• Primary Location: [Nairobi, Kenya] — Expectation of days in the office will be confirmed by your Hiring Manager
• Sprint Cadence: Fortnightly agile sprints
• Reporting Line: [Reports to Technical Program Manager, TPM] 3. Role Purpose We are looking for a Data Scientist to play a critical role in driving the data intelligence layer of the implementation programme. Embedded within a crossfunctional tech squad, the role is responsible for delivering propensity model deployment, customer segmentation, PANbased analytics, digital lift measurement, and insight dashboards that support datadriven acquisition, activation, and usage campaigns. The data scientist will work closely with Backend Engineers and the API Integration Engineer to operationalize data pipelines, and partner with the marketing and product teams to translate analytical outputs into actionable campaign targeting and measurement. 4. Key Responsibilities
• Define and implement a PAN (Primary Account Number) extraction and pseudonymization approach that supports targeted campaign analytics while adhering to data governance, PCI-DSS, and applicable data privacy regulations; document the data handling approach clearly.
• Design, validate, and deploy propensity models to identify high-potential customers for digital payment acquisition, activation, and usage campaigns — including Visa card adoption, Visa Direct usage, and tokenization uptake.
• Build customer segmentation frameworks that combine transactional, behavioural, and demographic signals to produce actionable cohorts for marketing and campaign teams.
• Develop and maintain a "digital lift" measurement framework, defining control/treatment group methodology, attribution logic, and statistical significance thresholds for evaluating campaign impact.
• Design and deliver analytics dashboards and reporting packs that provide stakeholders with clear, actionable visibility of campaign performance, model output, and digital adoption metrics.
• Collaborate with Backend Engineers to design and validate data pipelines that reliably feed analytical models with fresh, clean, and correctly structured data.
• Partner with the Frontend Engineer to align analytics event taxonomy and validate that app-level instrumentation is firing correctly and producing usable data.
• Support the Diaspora consumer proposition workstream with relevant analytical inputs, including diaspora remittance patterns, activation rates, and channel preference analysis.
• Conduct data quality assessments of source datasets; define data quality rules and escalate data issues to the engineering team for remediation.
• Document all models, methodologies, feature engineering approaches, and validation results in reproducible, peer-reviewable notebooks and technical reports.
• Deliver structured knowledge transfer to internal data and analytics team
• Maintain awareness of and compliance with all applicable data governance policies; escalate any data handling concerns to the Scrum Master and relevant stakeholders. 5. Measurable Outcomes & Deliverables First 30 Days
• Data landscape assessment completed: key data sources, access status, quality issues, and governance considerations documented.
• PAN handling and analytics data governance approach reviewed with data governance team; agreed approach documented.
• Propensity model scope and feature set defined; initial exploratory data analysis (EDA) completed.
• Digital lift measurement framework design (v1) produced and reviewed with client marketing/product stakeholders.
• Analytics event tracking requirements shared with Frontend Engineer; event taxonomy v1 agreed. Days 31–60
• Propensity model (v1) trained, validated, and output reviewed with stakeholders; model card produced documenting performance, limitations, and intended use.
• First customer segmentation cohort produced and delivered to campaign team; cohort definition and selection logic documented.
• Data pipeline (v1) for model feature ingestion operational in development / staging environment; data freshness and quality validated.
• Digital lift measurement baseline established for at least one active campaign or initiative.
• Analytics dashboard (v1) live, showing key digital adoption and campaign KPIs. Days 61–90
• Propensity model deployed to production / scoring environment; scoring pipeline operational with defined refresh cadence.
• At least one end-to-end campaign cycle measured using the digital lift framework; results reported to stakeholders with statistical confidence intervals.
• PAN-based analytics approach operationalized (within agreed governance framework); targeted campaign extract produced and delivered to campaign execution team.
• Diaspora consumer analytics input delivered: activation rate analysis, channel preference insights, and prioritization recommendations.
• Model and pipeline documentation completed; client data team onboarded to operate and retrain model. Ongoing KPIs
• Propensity model consistently meets agreed performance and stability thresholds at each refresh cycle
• Propensityscored customers align well with the intended behavioural cohorts when validated postcampaign
• Dashboard availability and data accuracy: ≥ 99% dashboard uptime; zero material data errors in executive-level reporting packs.
• Data governance compliance: zero data handling incidents escalated to privacy/compliance teams during engagement.
• Knowledge transfer: Internal data team able to independently run scoring pipeline and refresh model by end of engagement. 6. Stakeholders & Ways of Working Agile Ceremonies: All sprint ceremonies; leads data science story refinement; participates in daily stand-ups. Reporting Cadence:
• Sprint-level: analytics and modelling progress at sprint review.
• Monthly: campaign performance and digital lift summary to client marketing and