Data Science Specialist
2025-05-12T19:24:57+00:00
National Social Security Fund ( NSSF)
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https://www.greatugandajobs.com/jobs/
FULL_TIME
Plot 1 Pilkington Road, Workers House, 14th Floor
Headquarters
Kampala
00256
Uganda
Finance
Science & Engineering
2025-05-26T17:00:00+00:00
Uganda
8
Job Purpose:
The ideal candidate will have a strong background in Predictive Modeling, Machine Learning, Natural Language Processing, and Robotics. In this role, you will be responsible for deriving valuable insights from data to influence business decisions, improve processes, and develop advanced technologies. You'll work closely with cross-functional teams to contribute to the overall data strategy of the organization.
Duties and Responsibilities include:
- Develop, implement, and validate predictive models to forecast key business metrics, using techniques such as linear regression, logistic regression, time-series analysis, etc.
- Perform feature engineering to improve the performance of models.
- Continuously monitor and evaluate model performance and make adjustments as needed.
- Design and deploy machine learning algorithms to solve complex business problems, including but not limited to recommendation systems, customer segmentation, and fraud detection.
- Fine-tune and optimize existing machine learning models for better accuracy and efficiency.
- Collaborate with data engineers to deploy models into production.
- Develop NLP models for text classification, sentiment analysis, and chatbot functionalities.
- Experiment with different NLP techniques like word embeddings, transformer models, and language models to improve system performance.
- Collaborate with frontend developers to integrate NLP functionalities into applications.
- Collaborate with robotics engineers to integrate data-driven algorithms into robotic systems.
- Utilize sensor data for predictive maintenance of robotic equipment.
- Work on optimizing robotic movements and operations using reinforcement learning or other advanced techniques.
- Build interactive dashboards and reports to communicate findings and model outcomes to stakeholders.
- Use data visualization tools such as Tableau, Power BI, or custom libraries to make complex data more accessible.
- Work in cross-functional teams to understand business requirements and provide data-driven recommendations.
- Share knowledge and expertise in machine learning and data science within the organization through presentations, documentation, and training sessions.
- Emerging Technologies: Stay abreast of emerging technologies and algorithms in data science, machine learning, NLP, and robotics, and evaluate their potential impact on business objectives.
- Research & Development: Conduct in-house research to develop new data science techniques and algorithms. Test and validate research results using real-world data and scenarios.
- Collaborate with academic institutions and technology partners for joint research projects and knowledge exchange.
- Foster a culture of innovation by encouraging new ideas and experimentation. Validate and implement proof-of-concept projects that can be integrated into business solutions.
- Write research papers, articles, and reports to share findings both internally and externally. Present research findings at relevant scientific conferences and workshops to showcase organizational capabilities in advanced analytics and data science.
- Disseminate research findings to cross-functional teams and mentor staff in implementing cutting-edge technologies and techniques into existing frameworks.
- Develop, monitor, and track the efficiency of data science models against the desired business outcomes.
- Provide insight and input in agile scrum teams.
- Support department-wide initiatives like audits conducted by statutory bodies, risk, and internal audit departments.
Academic and Professional Qualifications:
- A bachelor’s degree in computer science, Statistics, Software Engineering, or any related field.
- Formal certification in Data Science, Big Data, or Data Analytics.
Work Experience:
- Minimum of 3 years of progressive working experience in data science and analytics role.
Additional Competencies:
- Familiarity with analytics, predictive modelling, and data management tools (e.g., R, Python).
- Ability to work under minimum supervision and as part of a team.
- Ability to work under pressure, adapt to change, and meet deadlines in a fast-paced environment.
- Excellent written and oral communication skills.
- Strong analytical and problem-solving skills.
- Proficiency in statistical modelling, analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization of algorithms.
- Excellent programming skills.
- Analytical and strategic thinking capabilities.
- Problem-solving abilities.
- Outstanding attention to detail.
- Planning and coordination skills.
Develop, implement, and validate predictive models to forecast key business metrics, using techniques such as linear regression, logistic regression, time-series analysis, etc. Perform feature engineering to improve the performance of models. Continuously monitor and evaluate model performance and make adjustments as needed. Design and deploy machine learning algorithms to solve complex business problems, including but not limited to recommendation systems, customer segmentation, and fraud detection. Fine-tune and optimize existing machine learning models for better accuracy and efficiency. Collaborate with data engineers to deploy models into production. Develop NLP models for text classification, sentiment analysis, and chatbot functionalities. Experiment with different NLP techniques like word embeddings, transformer models, and language models to improve system performance. Collaborate with frontend developers to integrate NLP functionalities into applications. Collaborate with robotics engineers to integrate data-driven algorithms into robotic systems. Utilize sensor data for predictive maintenance of robotic equipment. Work on optimizing robotic movements and operations using reinforcement learning or other advanced techniques. Build interactive dashboards and reports to communicate findings and model outcomes to stakeholders. Use data visualization tools such as Tableau, Power BI, or custom libraries to make complex data more accessible. Work in cross-functional teams to understand business requirements and provide data-driven recommendations. Share knowledge and expertise in machine learning and data science within the organization through presentations, documentation, and training sessions. Emerging Technologies: Stay abreast of emerging technologies and algorithms in data science, machine learning, NLP, and robotics, and evaluate their potential impact on business objectives. Research & Development: Conduct in-house research to develop new data science techniques and algorithms. Test and validate research results using real-world data and scenarios. Collaborate with academic institutions and technology partners for joint research projects and knowledge exchange. Foster a culture of innovation by encouraging new ideas and experimentation. Validate and implement proof-of-concept projects that can be integrated into business solutions. Write research papers, articles, and reports to share findings both internally and externally. Present research findings at relevant scientific conferences and workshops to showcase organizational capabilities in advanced analytics and data science. Disseminate research findings to cross-functional teams and mentor staff in implementing cutting-edge technologies and techniques into existing frameworks. Develop, monitor, and track the efficiency of data science models against the desired business outcomes. Provide insight and input in agile scrum teams. Support department-wide initiatives like audits conducted by statutory bodies, risk, and internal audit departments.
Familiarity with analytics, predictive modelling, and data management tools (e.g., R, Python). Ability to work under minimum supervision and as part of a team. Ability to work under pressure, adapt to change, and meet deadlines in a fast-paced environment. Excellent written and oral communication skills. Strong analytical and problem-solving skills. Proficiency in statistical modelling, analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization of algorithms. Excellent programming skills. Analytical and strategic thinking capabilities. Problem-solving abilities. Outstanding attention to detail. Planning and coordination skills.
A bachelor’s degree in computer science, Statistics, Software Engineering, or any related field. Formal certification in Data Science, Big Data, or Data Analytics. Work Experience: Minimum of 3 years of progressive working experience in data science and analytics role
JOB-68224b09c7889
Vacancy title:
Data Science Specialist
[Type: FULL_TIME, Industry: Finance, Category: Science & Engineering]
Jobs at:
National Social Security Fund ( NSSF)
Deadline of this Job:
Monday, May 26 2025
Duty Station:
Plot 1 Pilkington Road, Workers House, 14th Floor | Headquarters | Kampala | Uganda
Summary
Date Posted: Monday, May 12 2025, Base Salary: Not Disclosed
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JOB DETAILS:
Job Purpose:
The ideal candidate will have a strong background in Predictive Modeling, Machine Learning, Natural Language Processing, and Robotics. In this role, you will be responsible for deriving valuable insights from data to influence business decisions, improve processes, and develop advanced technologies. You'll work closely with cross-functional teams to contribute to the overall data strategy of the organization.
Duties and Responsibilities include:
- Develop, implement, and validate predictive models to forecast key business metrics, using techniques such as linear regression, logistic regression, time-series analysis, etc.
- Perform feature engineering to improve the performance of models.
- Continuously monitor and evaluate model performance and make adjustments as needed.
- Design and deploy machine learning algorithms to solve complex business problems, including but not limited to recommendation systems, customer segmentation, and fraud detection.
- Fine-tune and optimize existing machine learning models for better accuracy and efficiency.
- Collaborate with data engineers to deploy models into production.
- Develop NLP models for text classification, sentiment analysis, and chatbot functionalities.
- Experiment with different NLP techniques like word embeddings, transformer models, and language models to improve system performance.
- Collaborate with frontend developers to integrate NLP functionalities into applications.
- Collaborate with robotics engineers to integrate data-driven algorithms into robotic systems.
- Utilize sensor data for predictive maintenance of robotic equipment.
- Work on optimizing robotic movements and operations using reinforcement learning or other advanced techniques.
- Build interactive dashboards and reports to communicate findings and model outcomes to stakeholders.
- Use data visualization tools such as Tableau, Power BI, or custom libraries to make complex data more accessible.
- Work in cross-functional teams to understand business requirements and provide data-driven recommendations.
- Share knowledge and expertise in machine learning and data science within the organization through presentations, documentation, and training sessions.
- Emerging Technologies: Stay abreast of emerging technologies and algorithms in data science, machine learning, NLP, and robotics, and evaluate their potential impact on business objectives.
- Research & Development: Conduct in-house research to develop new data science techniques and algorithms. Test and validate research results using real-world data and scenarios.
- Collaborate with academic institutions and technology partners for joint research projects and knowledge exchange.
- Foster a culture of innovation by encouraging new ideas and experimentation. Validate and implement proof-of-concept projects that can be integrated into business solutions.
- Write research papers, articles, and reports to share findings both internally and externally. Present research findings at relevant scientific conferences and workshops to showcase organizational capabilities in advanced analytics and data science.
- Disseminate research findings to cross-functional teams and mentor staff in implementing cutting-edge technologies and techniques into existing frameworks.
- Develop, monitor, and track the efficiency of data science models against the desired business outcomes.
- Provide insight and input in agile scrum teams.
- Support department-wide initiatives like audits conducted by statutory bodies, risk, and internal audit departments.
Academic and Professional Qualifications:
- A bachelor’s degree in computer science, Statistics, Software Engineering, or any related field.
- Formal certification in Data Science, Big Data, or Data Analytics.
Work Experience:
- Minimum of 3 years of progressive working experience in data science and analytics role.
Additional Competencies:
- Familiarity with analytics, predictive modelling, and data management tools (e.g., R, Python).
- Ability to work under minimum supervision and as part of a team.
- Ability to work under pressure, adapt to change, and meet deadlines in a fast-paced environment.
- Excellent written and oral communication skills.
- Strong analytical and problem-solving skills.
- Proficiency in statistical modelling, analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization of algorithms.
- Excellent programming skills.
- Analytical and strategic thinking capabilities.
- Problem-solving abilities.
- Outstanding attention to detail.
- Planning and coordination skills.
Work Hours: 8
Experience in Months: 36
Level of Education: bachelor degree
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