Job Ref. No.: JLIL147
Position: Data Scientist
Jubilee Insurance was established in August 1937, as the first locally incorporated Insurance Company based in Mombasa.
Jubilee Insurance has spread its sphere of influence throughout the region to become the largest Composite insurer in
East Africa, handling Life, Pensions, General and Medical Insurance. Today, Jubilee is the number one insurer in East
Africa with over 450,000 clients. Jubilee Insurance has a network of offices in Kenya, Uganda, Tanzania, Burundi, and
Mauritius. It is the only ISO certified insurance group listed on the three East Africa stock exchanges – The Nairobi
Securities Exchange (NSE), Dar es Salaam Stock Exchange and Uganda Securities Exchange. Its regional offices are
highly rated on leadership, quality and risk management and have been awarded an AA- in Kenya and Uganda, and an
A+ in Tanzania. For more information, visit www.JubileeInsurance.com.
We currently have an exciting career opportunity for Data Scientist within Jubilee Life Insurance Limited. The position
holder will report to the Head of Data Science.
The Data Scientist is responsible for advising the business on the potential of data, to provide new insights into the
business’s mission, and through the use of advanced statistical analysis, data mining, and data visualization techniques,
to create solutions that enable enhanced business performance.
1. Execute ad-hoc data mining and exploratory statistics tasks on large datasets related to the business 'strategies.
2. Build econometric and statistical models for various problems inclusive of projections, classification, clustering,
pattern analysis, sampling and simulations.
3. Build the foundation of state-of-the-art scientific and technical capabilities within the Data Science department in
order to support several planned and ongoing data analytics projects.
4. Provide forward-thinking recommendations to the business by building in-depth understanding of the problem
domain and available business data assets, especially those pertaining to strategic initiatives and value-based
5. Generate actionable insights applying advanced statistical techniques, for example, predictive statistical models,
segmentation analysis, customer profiling, analysis, survey design, and data mining.
Corporate Governance (Regulatory and Compliance):
1. Participate in project execution to ensure that projects scope is understood, completed on time, to service delivery
2. Adhere to data privacy and security regulations, maintain confidentiality, and promote responsible data practices in
compliance with regulatory requirements and industry standards.
3. Ensure all assigned policies are ‘Read’ and assigned policy or procedures tests are undertaken and the lowest
acceptable score is 90%.
Leadership and Culture:
1. Build strong relationships and communications with stakeholders to ensure customer satisfaction.
2. Collaborate with data scientists to communicate obstacles and findings to relevant stakeholders in an effort to improve
decision making and drive business performance.
3. Continually improving ways of working within the team to drive efficient and impactful engagement and accurate
delivery of service.
Key Competencies and Skills
1. Demonstrate ability and passion for designing and implementing successful data analysis solutions within a business.
2. Ability to apply data-mining techniques in practical real-world business issues.
3. Skills in the workings of SQL and scripting languages such as Python
4. Familiarity with statistical analysis, data visualization, and data cleansing tools and techniques.
5. Strong skill in statistical techniques.
6. Familiarity in big data and standardizing and A/B testing.
1. Bachelor’s degree in Statistics, Mathematics, Computer Science, Machine Learning, Economics, or any other related
1. Big Data or Data Science certification from recognized institutions
1. At least 2 years of working experience working with business analysis/informatics and business outcomes research
within a fast-paced and complex business setting, preferably working as a data scientist.
2. Experience working in probability and statistics, time-series analysis, or econometrics.
3. Experience in the use of machine learning methods, for example, linear regression, decision trees.
4. Experience as well as in-depth knowledge of the Python/R programming language and Azure Machine Learning Studio.