Data Scientist

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

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