Data Scientist
2025-12-23T13:02:38+00:00
Co-operative Bank of Kenya
https://cdn.greatkenyanjobs.com/jsjobsdata/data/employer/comp_7942/logo/Coopbanklogo.jpg
http://www.co-opbank.co.ke/
FULL_TIME
Nairobi
Nairobi
00100
Kenya
Banking
Science & Engineering, Computer & IT, Business Operations
2025-12-31T17:00:00+00:00
8
Background information about the job or company (e.g., role context, company overview)
You have an edge as far as data analytics is concerned; you stay on top of the latest analytics insights and trends, reimagining the possible within data analytics and analytical innovation. You think in terms of possibilities, opportunities and discoveries and have found your niche in the breadth and depth of advanced analytics that wow.
Are you looking for an employer who promotes individual excellence and mutual respect in a team-driven culture with a key focus on social empowerment? Look no further; Make your move towards analytical innovation within the Co-operative Bank of Kenya, “The Kingdom Bank” the place for those looking to new horizons.
The Data Scientist position is the perfect opportunity for you. You will be a pivotal member, reporting to Head – Business Intelligence. You will be responsible for applying data mining techniques, doing statistical analysis, building high quality prediction algorithms, developing analytical reports and devising analytical solutions to use cases and data science problems. The primary requirement is not related to traditional programming or systems analysis skills but to the ability to create sophisticated, value-added analytic systems that support revenue generation, risk management, operational efficiency, regulatory compliance, portfolio management, and research.
The role
Responsibilities or duties
The successful jobholder will be expected to:
- Perform statistical analysis; deploying models on large data sets.
- Conduct exploratory data analysis
- Source data, Design, develop, and implement predictive models based on machine-learning and AI algorithms
- Evaluate existing model performance by calculating and publishing monthly Performance Metric foe each deployed model
- Transform large, complex datasets into pragmatic, actionable insights, leverage data to identify, quantify and influence tangible business gain by performing ad-hoc analysis and presenting results in reports, dashboards and charts.
- Transform raw data into meaningful features that enhance model performance
- Implement analytical model designs, perform any restructuring required, and review dataset implementations performed by the data engineer and BI developers.
- Selecting features, building and optimizing classifiers using machine learning techniques and Data mining using bank selected data mining tools.
- Enhance data collection procedures to include information that is relevant for building analytical systems, process, cleanse and verify the integrity of data used for advanced analysis by creating automated anomaly detection systems and constant tracking of its performance.
- Improve data foundational procedures, guidelines and standards and develop best practices for data management, maintenance, reporting and security.
- Implement statistical data quality procedures or test-driven approach for quality assurance and conduct performance tuning to be able to optimize the application of statistical models and scripts.
- Design, build, and maintain various parts of the data warehousing with respect to requirements gathering, data modeling, metric establishment, reporting production, and data visualization.
- Gather and process raw, unstructured data at scale into a form suitable for analysis then consolidate into the data warehouse in order to perform Business Intelligence and advanced analytics.
- Evaluate datasets for accuracy and quality using statistical data quality procedures, software, or test-driven approaches that ensure quality assurance and solve any issues which may arise.
- Assist to analyze business/use case requirements from BI analysts to determine operational problems, define data modeling requirements, gather and validate information, apply judgment and statistical tests and develop data structures to support the generation of business insights and strategy;
- Provide test interfaces for users to test the reports and dashboards before being put on the production environment and carry out technical user training as required to enable users interpret BI solutions.
- Develop and maintain documentation/manuals on models developed, reports generated and statistical solutions devised.
- Assist in developing and implementing a program of continuous improvement of BI processes through a cycle of analysis of existing systems, processes, and tools, identifying areas for improvement, and implementing high-impact changes, and getting feedback from stakeholders.
Qualifications or requirements (e.g., education, skills)
Experience needed
Any other provided details (e.g., benefits, work environment, team info, or additional notes)
The successful jobholder will be required to possess the following qualifications: –
- A degree in mathematics, statistics, data sciences or related quantitative fields.
- A minimum 3-5 years of business experience as a Data Scientist or applied data experience is required.
- Experience with relational databases such as Oracle, SQL queries, or OLAP cubes is preferred.
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM and Decision Forests
- Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable
- Proficiency in using query languages such as SQL, Hive, Pig and Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
- Good applied statistics skills, such as distributions, statistical testing, regression, etc. with good scripting and programming skills
- Knowledge of agile software development process and performance metric tools
- Experience extracting and cleaning text in different formats e.g. HTML, pdf files
- Proven ability to collaborate with other team members across boundaries and contribute productively to the team’s work and output, demonstrating respect for different points of view. Able to use strong interpersonal and teamwork skills to cultivate effective, productive client relationships and partnerships across organizational boundaries.
- Strong co-ordination and project management skills to handle complex projects
* Perform statistical analysis; deploying models on large data sets. * Conduct exploratory data analysis * Source data, Design, develop, and implement predictive models based on machine-learning and AI algorithms * Evaluate existing model performance by calculating and publishing monthly Performance Metric foe each deployed model * Transform large, complex datasets into pragmatic, actionable insights, leverage data to identify, quantify and influence tangible business gain by performing ad-hoc analysis and presenting results in reports, dashboards and charts. * Transform raw data into meaningful features that enhance model performance * Implement analytical model designs, perform any restructuring required, and review dataset implementations performed by the data engineer and BI developers. * Selecting features, building and optimizing classifiers using machine learning techniques and Data mining using bank selected data mining tools. * Enhance data collection procedures to include information that is relevant for building analytical systems, process, cleanse and verify the integrity of data used for advanced analysis by creating automated anomaly detection systems and constant tracking of its performance. * Improve data foundational procedures, guidelines and standards and develop best practices for data management, maintenance, reporting and security. * Implement statistical data quality procedures or test-driven approach for quality assurance and conduct performance tuning to be able to optimize the application of statistical models and scripts. * Design, build, and maintain various parts of the data warehousing with respect to requirements gathering, data modeling, metric establishment, reporting production, and data visualization. * Gather and process raw, unstructured data at scale into a form suitable for analysis then consolidate into the data warehouse in order to perform Business Intelligence and advanced analytics. * Evaluate datasets for accuracy and quality using statistical data quality procedures, software, or test-driven approaches that ensure quality assurance and solve any issues which may arise. * Assist to analyze business/use case requirements from BI analysts to determine operational problems, define data modeling requirements, gather and validate information, apply judgment and statistical tests and develop data structures to support the generation of business insights and strategy; * Provide test interfaces for users to test the reports and dashboards before being put on the production environment and carry out technical user training as required to enable users interpret BI solutions. * Develop and maintain documentation/manuals on models developed, reports generated and statistical solutions devised. * Assist in developing and implementing a program of continuous improvement of BI processes through a cycle of analysis of existing systems, processes, and tools, identifying areas for improvement, and implementing high-impact changes, and getting feedback from stakeholders.
* Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM and Decision Forests * Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable * Proficiency in using query languages such as SQL, Hive, Pig and Experience with NoSQL databases, such as MongoDB, Cassandra, HBase * Good applied statistics skills, such as distributions, statistical testing, regression, etc. with good scripting and programming skills * Knowledge of agile software development process and performance metric tools * Experience extracting and cleaning text in different formats e.g. HTML, pdf files * Proven ability to collaborate with other team members across boundaries and contribute productively to the team’s work and output, demonstrating respect for different points of view. Able to use strong interpersonal and teamwork skills to cultivate effective, productive client relationships and partnerships across organizational boundaries. * Strong co-ordination and project management skills to handle complex projects
* A degree in mathematics, statistics, data sciences or related quantitative fields. * A minimum 3-5 years of business experience as a Data Scientist or applied data experience is required. * Experience with relational databases such as Oracle, SQL queries, or OLAP cubes is preferred.
JOB-694a92ee0e291
Vacancy title:
Data Scientist
[Type: FULL_TIME, Industry: Banking, Category: Science & Engineering, Computer & IT, Business Operations]
Jobs at:
Co-operative Bank of Kenya
Deadline of this Job:
Wednesday, December 31 2025
Duty Station:
Nairobi | Nairobi
Summary
Date Posted: Tuesday, December 23 2025, Base Salary: Not Disclosed
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JOB DETAILS:
Background information about the job or company (e.g., role context, company overview)
You have an edge as far as data analytics is concerned; you stay on top of the latest analytics insights and trends, reimagining the possible within data analytics and analytical innovation. You think in terms of possibilities, opportunities and discoveries and have found your niche in the breadth and depth of advanced analytics that wow.
Are you looking for an employer who promotes individual excellence and mutual respect in a team-driven culture with a key focus on social empowerment? Look no further; Make your move towards analytical innovation within the Co-operative Bank of Kenya, “The Kingdom Bank” the place for those looking to new horizons.
The Data Scientist position is the perfect opportunity for you. You will be a pivotal member, reporting to Head – Business Intelligence. You will be responsible for applying data mining techniques, doing statistical analysis, building high quality prediction algorithms, developing analytical reports and devising analytical solutions to use cases and data science problems. The primary requirement is not related to traditional programming or systems analysis skills but to the ability to create sophisticated, value-added analytic systems that support revenue generation, risk management, operational efficiency, regulatory compliance, portfolio management, and research.
The role
Responsibilities or duties
The successful jobholder will be expected to:
- Perform statistical analysis; deploying models on large data sets.
- Conduct exploratory data analysis
- Source data, Design, develop, and implement predictive models based on machine-learning and AI algorithms
- Evaluate existing model performance by calculating and publishing monthly Performance Metric foe each deployed model
- Transform large, complex datasets into pragmatic, actionable insights, leverage data to identify, quantify and influence tangible business gain by performing ad-hoc analysis and presenting results in reports, dashboards and charts.
- Transform raw data into meaningful features that enhance model performance
- Implement analytical model designs, perform any restructuring required, and review dataset implementations performed by the data engineer and BI developers.
- Selecting features, building and optimizing classifiers using machine learning techniques and Data mining using bank selected data mining tools.
- Enhance data collection procedures to include information that is relevant for building analytical systems, process, cleanse and verify the integrity of data used for advanced analysis by creating automated anomaly detection systems and constant tracking of its performance.
- Improve data foundational procedures, guidelines and standards and develop best practices for data management, maintenance, reporting and security.
- Implement statistical data quality procedures or test-driven approach for quality assurance and conduct performance tuning to be able to optimize the application of statistical models and scripts.
- Design, build, and maintain various parts of the data warehousing with respect to requirements gathering, data modeling, metric establishment, reporting production, and data visualization.
- Gather and process raw, unstructured data at scale into a form suitable for analysis then consolidate into the data warehouse in order to perform Business Intelligence and advanced analytics.
- Evaluate datasets for accuracy and quality using statistical data quality procedures, software, or test-driven approaches that ensure quality assurance and solve any issues which may arise.
- Assist to analyze business/use case requirements from BI analysts to determine operational problems, define data modeling requirements, gather and validate information, apply judgment and statistical tests and develop data structures to support the generation of business insights and strategy;
- Provide test interfaces for users to test the reports and dashboards before being put on the production environment and carry out technical user training as required to enable users interpret BI solutions.
- Develop and maintain documentation/manuals on models developed, reports generated and statistical solutions devised.
- Assist in developing and implementing a program of continuous improvement of BI processes through a cycle of analysis of existing systems, processes, and tools, identifying areas for improvement, and implementing high-impact changes, and getting feedback from stakeholders.
Qualifications or requirements (e.g., education, skills)
Experience needed
Any other provided details (e.g., benefits, work environment, team info, or additional notes)
The successful jobholder will be required to possess the following qualifications: –
- A degree in mathematics, statistics, data sciences or related quantitative fields.
- A minimum 3-5 years of business experience as a Data Scientist or applied data experience is required.
- Experience with relational databases such as Oracle, SQL queries, or OLAP cubes is preferred.
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM and Decision Forests
- Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable
- Proficiency in using query languages such as SQL, Hive, Pig and Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
- Good applied statistics skills, such as distributions, statistical testing, regression, etc. with good scripting and programming skills
- Knowledge of agile software development process and performance metric tools
- Experience extracting and cleaning text in different formats e.g. HTML, pdf files
- Proven ability to collaborate with other team members across boundaries and contribute productively to the team’s work and output, demonstrating respect for different points of view. Able to use strong interpersonal and teamwork skills to cultivate effective, productive client relationships and partnerships across organizational boundaries.
- Strong co-ordination and project management skills to handle complex projects
Work Hours: 8
Experience in Months: 36
Level of Education: bachelor degree
Job application procedure
Interested in applying for this job? Click here to submit your application now.
If you are confident that you fit the role and person profile and you are keen to add value to your career then please forward your application letter enclosing detailed Curriculum Vitae indicating the job reference number DS2/IID/2025 before the close of business on 31st December 2025.
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