Commercial Data Analyst
AI summary
Mid-level Commercial Data Analyst role based in Nairobi, Kenya, with an estimated salary of KES 120,000–200,000/month. The position is on-site and full-time, focused on Business Intelligence, commercial analytics, and enterprise dashboard development. Candidates should have 3–5 years of relevant experience and a degree in a quantitative or business-related field.
- Estimated salary KES 120,000–200,000/month
- On-site full-time role in Nairobi
- Mid-level position requiring 3–5 years of experience
- Focus on Business Intelligence, commercial analytics, and dashboards
- Relevant degree in Statistics, Mathematics, Data Science, or related field required
AI job guide
Use this guide to check salary signals, requirements, documents, application steps and safety before you apply.
AI salary guide
Source salary availableThe source lists Est. KES 120,000 – 200,000/mo. Confirm the final pay, benefits, contract terms and allowances directly with the employer before accepting an offer.
Can you qualify for this role?
- Required3+ years of relevant experienceThe job post includes a minimum experience signal.
- RequiredEducation or certification mentioned in the postThe captured text mentions education, a diploma, certificate, or licence.
- PreferredPractical evidence in sales, technology, Data AnalystThe tags and summary point to skills connected with this role.
- RequiredAvailability to work in Not specifiedThe vacancy is associated with this location.
- UnclearComfort with the Full-time contract termsConfirm hours, duration, probation and benefits at the original source.
Documents to prepare
- Likely requiredUpdated CV
- Role specificCover letter or short employer message
- OptionalProfessional references
- Role specificAcademic or professional certificates
- VerifyID or passport only after verifying the employer
Application tips for this job
- Place your strongest Commercial Data Analyst evidence in the first half of your CV.
- In your cover letter or employer message, connect your experience to GoKazini and the role in Not specified.
- Add concrete examples related to sales, technology, Data Analyst, ideally with measurable outcomes or clear responsibilities.
- Follow the instructions from GoKazini; avoid sending documents to unofficial contacts or copied links.
- Confirm the deadline, interview location and employer contact before sharing personal documents.
Source and safety check
- GoKazini
- Original source link available
- Application method is clear
- Deadline not specified
- No major risk signal was detected in the captured text.
Never pay for interviews, shortlisting, medical checks, uniforms, or job placement. Confirm every application at the original source before sharing personal documents. Report suspicious listing.
Interview preparation
- What experience makes you a strong fit for this Commercial Data Analyst role in sales, technology?
- How have you handled responsibilities similar to those in this job post?
- Are you available to work in Not specified under the listed contract or schedule?
- Prepare examples with clear responsibilities, tools used and measurable outcomes.
- Review the source and research GoKazini before the interview.
Ask what the first priorities will be in the role and how success will be measured.
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Original source description
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- ML
- Commercial Data Analyst
- Members only
- Nairobi, Kenya
- Est. KES 120,000 – 200,000/mo
- Est.
- On-site
- Full-time
- Mid-level
- Posted
- today
- Share
- What you bring
- Qualifications
- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Business Analytics, Economics, Information Systems, or a related field.
- Professional certifications in Business Intelligence, Data Analytics, or Data Visualization are an added advantage.
Experience
- Minimum of 3–5 years'
- in Business Intelligence, Commercial Analytics, or Data Analytics.
- supporting commercial, sales, marketing, customer growth, or retention functions.
- Demonstrated
- developing and maintaining enterprise dashboards.
- working with large and complex datasets from multiple systems.