{"id":2901799,"url":"https://alion.io/job/paytech-nexus-data-scientist-2","title":"Data Scientist","company":{"id":4433588,"name":"PayTech Nexus","domain":"paytechnexus.com","url":"https://alion.io/company/paytech-nexus","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Nairobi, Kenya"],"countries":["KE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":29000,"max_usd":76000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2491},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"dbt","optional":false},{"name":"LightGBM","optional":false},{"name":"Looker","optional":false},{"name":"PCI DSS","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Scrum","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"Tableau","optional":false},{"name":"Tokenization","optional":false},{"name":"XGBoost","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Confluence","optional":true},{"name":"Git","optional":true},{"name":"GitHub","optional":true},{"name":"GitLab","optional":true},{"name":"Jira","optional":true},{"name":"MLFlow","optional":true},{"name":"SharePoint","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-20T06:40:57Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-20T20:09:40Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Title: Data Scientist\nTerm: 12 months\nLocation: Nairobi, Kenya\nReports to: Technical Program Manager\nLevel: Senior (4-7 years of relevant experience)\nRole Purpose\nWe 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.\nKey Responsibilities\nDefine 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.\nDesign, 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.\nBuild customer segmentation frameworks that combine transactional, behavioural, and demographic signals to produce actionable cohorts for marketing and campaign teams.\nDevelop and maintain a \"digital lift\" measurement framework, defining control/treatment group methodology, attribution logic, and statistical significance thresholds for evaluating campaign impact.\nDesign and deliver analytics dashboards and reporting packs that provide stakeholders with clear, actionable visibility of campaign performance, model output, and digital adoption metrics.\nCollaborate with Backend Engineers to design and validate data pipelines that reliably feed analytical models with fresh, clean, and correctly structured data.\nPartner with the Frontend Engineer to align analytics event taxonomy and validate that app-level instrumentation is firing correctly and producing usable data.\nSupport the Diaspora consumer proposition workstream with relevant analytical inputs, including diaspora remittance patterns, activation rates, and channel preference analysis.\nConduct data quality assessments of source datasets; define data quality rules and escalate data issues to the engineering team for remediation.\nDocument all models, methodologies, feature engineering approaches, and validation results in reproducible, peer-reviewable notebooks and technical reports.\nDeliver structured knowledge transfer to internal data and analytics team\nMaintain awareness of and compliance with all applicable data governance policies; escalate any data handling concerns to the Scrum Master and relevant stakeholders.\nMeasurable Outcomes & Deliverables\nFirst 30 Days\nData landscape assessment completed: key data sources, access status, quality issues, and governance considerations documented.\nPAN handling and analytics data governance approach reviewed with data governance team; agreed approach documented.\nPropensity model scope and feature set defined; initial exploratory data analysis (EDA) completed.\nDigital lift measurement framework design (v1) produced and reviewed with client marketing/product stakeholders.\nAnalytics event tracking requirements shared with Frontend Engineer; event taxonomy v1 agreed.\nDays 31-60\nPropensity model (v1) trained, validated, and output reviewed with stakeholders; model card produced documenting performance, limitations, and intended use.\nFirst customer segmentation cohort produced and delivered to campaign team; cohort definition and selection logic documented.\nData pipeline (v1) for model feature ingestion operational in development / staging environment; data freshness and quality validated.\nDigital lift measurement baseline established for at least one active campaign or initiative.\nAnalytics dashboard (v1) live, showing key digital adoption and campaign KPIs.\nDays 61-90\nPropensity model deployed to production / scoring environment; scoring pipeline operational with defined refresh cadence.\nAt least one end-to-end campaign cycle measured using the digital lift framework; results reported to stakeholders with statistical confidence intervals.\nPAN-based analytics approach operationalized (within agreed governance framework); targeted campaign extract produced and delivered to campaign execution team.\nDiaspora consumer analytics input delivered: activation rate analysis, channel preference insights, and prioritization recommendations.\nModel and pipeline documentation completed; client data team onboarded to operate and retrain model.\nOngoing KPIs\nPropensity model consistently meets agreed performance and stability thresholds at each refresh cycle\nPropensityscored customers align well with the intended behavioural cohorts when validated postcampaign\nDashboard availability and data accuracy: 99% dashboard uptime; zero material data errors in executive-level reporting packs.\nData governance compliance: zero data handling incidents escalated to privacy/compliance teams during engagement.\nKnowledge transfer: Internal data team able to independently run scoring pipeline and refresh model by end of engagement.\nStakeholders & Ways of Working\nAgile Ceremonies: All sprint ceremonies; leads data science story refinement; participates in daily stand-ups.\nReporting Cadence:\nSprint-level: analytics and modelling progress at sprint review.\nMonthly: campaign performance and digital lift summary to client marketing and senior stakeholders.\nAd-hoc: data quality or governance escalations to TPM and internal data governance team.\nCross-Functional Touchpoints:\nBackend Engineers (data pipeline design and delivery).\nFrontend Engineer (analytics instrumentation validation).\nMarketing and campaign teams (cohort delivery, campaign measurement).\nData governance / privacy team (data handling approvals).\nRequired Skills & Experience\n6+ years of data science experience, with at least 4 years in payments, fintech, financial services, or telecoms.\nProven experience deploying propensity models or classification models in a production or near-production environment; familiarity with the full ML lifecycle (EDA, feature engineering, training, validation, deployment, monitoring).\nStrong experience with customer segmentation methodologies and campaign analytics.\nDemonstrated understanding of PAN-based analytics approaches and the associated data governance, PCI-DSS, and privacy requirements; ability to design compliant analytical frameworks.\nProficiency in Python (pandas, scikit-learn, XGBoost/LightGBM, statsmodels) and/or R for analytical modelling.\nExperience designing and interpreting A/B tests and causal inference frameworks for digital lift measurement.\nAbility to build and maintain data pipelines using SQL, dbt, Airflow, or equivalent tools.\nExperience producing clear, stakeholder-ready insight reports and dashboards (Tableau, Power BI, Looker, or equivalent).\nStrong data quality assessment skills; experience defining and enforcing data quality rules.\nExcellent communication skills; ability to explain complex analytical outputs to non-technical stakeholders.\nPreferred / Nice-to-Have Skills\nExperience with mobile money or digital payment customer analytics (M-Pesa or comparable platforms).\nFamiliarity with diaspora remittance analytics or cross-border payment customer behaviour.\nKnowledge of differential privacy or anonymisation techniques applicable to payment data.\nExperience with MLOps tooling for model deployment and monitoring (MLflow / Vertex AI / SageMaker / equivalent).\nExperience in emerging markets data contexts (data sparsity, network effects, airtime credit proxies, etc.).\nTools & Technologies\nLanguages: Python (pandas, scikit-learn, XGBoost, LightGBM, statsmodels), SQL\nData pipelines: dbt, Apache Airflow, or equivalent\nDashboarding: Tableau, Power BI, or equivalent\nNotebooks: Jupyter or equivalent\nVersion control: Git (GitHub / GitLab)\nCloud: Azure or equivalent\nCollaboration: Confluence / SharePoint\nIssue tracking: Jira / Azure DevOps","description_format":"text","description_chars":8280,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Kenya","iso":"KE","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-20T06:40:57Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":27,"reasons":["conf:3","win:early"],"computed_at":"2026-10-20T23:34:32Z"},"pay":null,"html_url":"https://alion.io/job/paytech-nexus-data-scientist-2","json_url":"https://alion.io/job/paytech-nexus-data-scientist-2.json","meta":{"generated_at":"2026-10-20T23:34:32Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":27189,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-21T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4433588},"rest":"https://alion.io/mcp/rest/get_company?id=4433588"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fpaytech-nexus-data-scientist-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fpaytech-nexus-data-scientist-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fpaytech-nexus-data-scientist-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/paytech-nexus-data-scientist-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fpaytech-nexus-data-scientist-2"}]}