Data Scientist

• Data Scientist
• Anonymous Employer
• Engineering & Technology
• 2 months ago
• Easy apply
• 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
• Length:
• 4 years
• Language Requirement:
• English
• Working Hours:
• Contract - 8 to 5
• Applicant
• Location:
• Kenya
• Job descriptions &
• ·
• 6+
• years of data science experience, with at least 4 years in payments, fintech,
• financial services, or telecoms.
• ·
• Proven
• 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).
• ·
• Strong
• with customer segmentation methodologies and campaign analytics.
• ·
• Demonstrated
• understanding of PAN-based analytics approaches and the associated data
• governance, PCI-DSS, and privacy requirements; ability to design compliant
• analytical frameworks.
• ·
• Proficiency
• in Python (pandas, scikit-learn, XGBoost/LightGBM, statsmodels) and/or R for analytical
• modelling.
• ·
• designing and interpreting A/B tests and causal inference frameworks for
• digital lift measurement.
• ·
• Ability
• to build and maintain data pipelines using SQL, dbt, Airflow, or equivalent
• tools.
• ·
• producing clear, stakeholder-ready insight reports and dashboards (Tableau,
• Power BI, Looker, or equivalent).
• ·
• Strong
• data quality assessment skills;
• defining and enforcing data quality
• rules.
• ·
• Excellent
• communication skills; ability to explain complex analytical outputs to
• non-technical stakeholders.
• 8.
• with mobile money or digital payment customer analytics (M-Pesa or comparable
• platforms).
• ·
• Familiarity
• with diaspora remittance analytics or cross-border payment customer behaviour.
• ·
• Knowledge
• of differential privacy or anonymisation techniques applicable to payment data.
• ·
• with MLOps tooling for model deployment and monitoring (MLflow / Vertex AI /
• SageMaker / equivalent).
• ·
• in emerging markets data contexts (data sparsity, network effects, airtime
• credit proxies, etc.).
• 9.
• Tools & Technologies
• ·
• Languages:
• Python (pandas, scikit-learn, XGBoost, LightGBM, statsmodels), SQL
• ·
• Data
• pipelines: dbt, Apache Airflow, or equivalent
• ·
• Dashboarding:
• Tableau, Power BI, or equivalent
• ·
• Notebooks:
• Jupyter or equivalent
• ·
• Version
• control: Git (GitHub / GitLab)
• ·
• Cloud:
• Azure or equivalent
• ·
• Collaboration:
• Confluence / SharePoint
• ·
• Issue
• tracking: Jira / Azure DevOps
• 10.
• Contract/Secondment Notes
• ·
• This
• is a contract/secondment engagement. Given the sensitive nature of payment and
• customer data handled in this role, the resource must rigorously comply with
• all applicable data protection legislation, PCI-DSS requirements, and
• client/Visa data governance policies. Any uncertainty regarding permissible
• data use must be escalated immediately.
• ·
• Performance
• will be assessed on a deliverables basis, with formal reviews at 30, 60, and 90
• days.
• ·
• The
• resource is expected to transfer model ownership, pipeline operations
• knowledge, and measurement methodology to client's in-house data team prior to
• engagement conclusion.
• Log In and Apply
• Important safety tips
• Do not make any payment without confirming with the BrighterMonday Customer Support Team.
• If you think this advert is not genuine, please report it via the Report Job link below.
• Report Job

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
• 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 senior
• stakeholders.
• ·
• Ad-hoc:
• data quality or governance escalations to TPM and internal data governance
• team.
• Cross-Functional Touchpoints:
• ·
• Backend
• Engineers (data pipeline design and delivery).
• ·
• Frontend
• Engineer (analytics instrumentation validation).
• ·
• Marketing
• and campaign teams (cohort delivery, campaign measurement).
• ·
• Data
• governance / privacy team (data handling approvals).
• 7.
• Required Skills &

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

Preferred Qualifications

• / Nice-to-Have Skills
• ·