M-Kopa
Closes in 2 days
Location: Kigali, Rwanda
Type: full-time
Job Description
Analyst role at M-KOPA to build underwriting frameworks and improve loan eligibility and pricing for millions of customers. Work with cross-functional teams (engineers, data scientists, growth and commercial) to drive growth while managing credit risk and margins. Remote or hybrid within UTC-1 to UTC+3.
Key Responsibilities
Analyze repayments and other data sources to refine eligibility criteria and manage credit risk.
Refine loan pricing using credit analysis and observed customer behavior.
Test new loan products to measure demand and credit performance.
Monitor credit performance for risk shifts and quantify margin impact.
Test predictive power of new datasets for eligibility.
Use Python, SQL, and related tools for analysis.
Collaborate with data scientists to integrate ML models into eligibility decisions.
Job Requirements
Several years in roles with significant analytical responsibilities.
Strong statistical modeling and quantitative analysis skills, including work with unstructured data.
Fluency in Python and SQL (and other relevant data tools).
Ability to translate complex data insights into actionable business strategy.
Strong written, oral, and visual data communication skills.
Proven ability to work cross-functionally with product, engineering, and commercial teams.
Strong interpersonal and collaboration skills.
Education
Not explicitly specified in the posting. Typical backgrounds include degrees in Economics, Statistics, Mathematics, Data Science, Computer Science, Engineering, or related quantitative disciplines. (If degree is required by local hiring rules, M-KOPA will verify academic qualifications during pre-employment checks.)
Experience
Several years of practical analytical experience (exact number not specified).
Experience applying statistical/quantitative methods to business problems and running end-to-end analyses.
Experience using Python and SQL in production or research contexts.
Good to Have / Nice to Have
Direct experience in credit, underwriting, or lending analytics.
Familiarity with machine learning models used for credit decisions.
Experience working in emerging markets or with underserved customer segments.
Location & Working Hours
Remote or hybrid roles accepted.
Candidate must be located in time zones UTC-1 to UTC+3 for effective collaboration across locations.
Application Deadline & Process
Applications reviewed on a rolling basis.
Shortlisting and interviews may occur at any stage; vacancy may close early if a suitable candidate is selected.
Successful candidates undergo pre-employment background checks (criminal records, ID, academic qualifications, employment dates, references).
No fees are charged at any stage of recruitment.
Benefits & Culture
Career development programs, coaching, on-the-job training.
Family-friendly policies, wellbeing focus, and flexible work.
Low-ego, diverse, collaborative environment emphasizing social impact and data-driven decision making.
click here to apply: https://jobs.ashbyhq.com/M-KOPA/8a3cdce8-d31b-44e1-b69a-121bf27685da
Job Description
Analyst role at M-KOPA to build underwriting frameworks and improve loan eligibility and pricing for millions of customers. Work with cross-functional teams (engineers, data scientists, growth and commercial) to drive growth while managing credit risk and margins. Remote or hybrid within UTC-1 to UTC+3.
Key Responsibilities
Analyze repayments and other data sources to refine eligibility criteria and manage credit risk.
Refine loan pricing using credit analysis and observed customer behavior.
Test new loan products to measure demand and credit performance.
Monitor credit performance for risk shifts and quantify margin impact.
Test predictive power of new datasets for eligibility.
Use Python, SQL, and related tools for analysis.
Collaborate with data scientists to integrate ML models into eligibility decisions.
Job Requirements
Several years in roles with significant analytical responsibilities.
Strong statistical modeling and quantitative analysis skills, including work with unstructured data.
Fluency in Python and SQL (and other relevant data tools).
Ability to translate complex data insights into actionable business strategy.
Strong written, oral, and visual data communication skills.
Proven ability to work cross-functionally with product, engineering, and commercial teams.
Strong interpersonal and collaboration skills.
Education
Not explicitly specified in the posting. Typical backgrounds include degrees in Economics, Statistics, Mathematics, Data Science, Computer Science, Engineering, or related quantitative disciplines. (If degree is required by local hiring rules, M-KOPA will verify academic qualifications during pre-employment checks.)
Experience
Several years of practical analytical experience (exact number not specified).
Experience applying statistical/quantitative methods to business problems and running end-to-end analyses.
Experience using Python and SQL in production or research contexts.
Good to Have / Nice to Have
Direct experience in credit, underwriting, or lending analytics.
Familiarity with machine learning models used for credit decisions.
Experience working in emerging markets or with underserved customer segments.
Location & Working Hours
Remote or hybrid roles accepted.
Candidate must be located in time zones UTC-1 to UTC+3 for effective collaboration across locations.
Application Deadline & Process
Applications reviewed on a rolling basis.
Shortlisting and interviews may occur at any stage; vacancy may close early if a suitable candidate is selected.
Successful candidates undergo pre-employment background checks (criminal records, ID, academic qualifications, employment dates, references).
No fees are charged at any stage of recruitment.
Benefits & Culture
Career development programs, coaching, on-the-job training.
Family-friendly policies, wellbeing focus, and flexible work.
Low-ego, diverse, collaborative environment emphasizing social impact and data-driven decision making.
click here to apply: https://jobs.ashbyhq.com/M-KOPA/8a3cdce8-d31b-44e1-b69a-121bf27685da
