494 Data Specialists jobs in Nairobi

About CHANCEN International
Chancen International is a non-profit financial institution providing ethical, fair, and responsible financing to help young Africans access quality tertiary education. Using an innovative Income Share Agreement (ISA) model, we cover students' tuition fees, allowing them to study without financial barriers. Graduates repay a portion of their income once employed, creating a sustainable funding cycle that supports future generations. By empowering young people with education, we enable their transition to meaningful employment, fostering economic mobility and strengthening communities.

Headquartered in Kigali, Rwanda, and operating in Rwanda, Kenya, South Africa, and Ghana, Chancen International has helped over 5,000 young people access education leading to dignified work. Our focus on marginalized populations ensures these youth gain the skills needed to thrive and contribute to building strong economies and peaceful nations.

We are committed to creating an inclusive and positive working environment where each individual can flourish and achieve their full potential. Our actions are guided by our company values: Authenticity, Curiosity, Courage, Stewardship and Fairness.

**Job Summary

The Research & Data Specialist will play a key role in strengthening Chancen's ISA underwriting and portfolio analytics by ensuring high-quality data input, interpreting macroeconomic and market trends, and integrating external datasets. This role sits within the Financial Planning and Analysis team and supports pricing, risk analysis, and portfolio optimization efforts. The Specialist will work closely with the Tech, Finance, and Impact teams to improve how Chancen collects, organizes, and analyzes data across countries and portfolios.
Key Responsibilities**
1. Data Input & Quality Assurance

• Maintain standardized input templates (ISA, PEI, employment); enforce schema, validation, and audit trails.
• Run QA checks (completeness, outliers, duplicates, cohort consistency); reconcile discrepancies with data owners.

• Publish data quality reports and escalate recurring issues with corrective actions.

• Underwriting Support

• Ingest and harmonize PEI datasets (enrolment, completion, placement, salary distributions/percentiles).

• Compute placement rate, time-to-placement, salary progression curves by program/PEI/country.

• Produce quarterly PEI scorecards to inform pricing guardrails (share %, thresholds, caps) and affordability checks.

• Market & Macro Trend Analysis

• Track inflation, minimum wage, unemployment, sector demand, and youth income trends; maintain a macro indicator pack per country.

• Write concise insight briefs (one-pager) highlighting risk/opportunity and recommended parameter changes.

• Country Partnerships & Data Procurement

• Build strong working relationships with Country Managers, PEIs, ministries/labour bureaus, and relevant agencies across different countries.

• Map, source, and procure priority datasets (employment, wage, placement, census/sector data, PEI admin data); maintain a live source register with refresh cadences and owners.
• Lead data-sharing requests and negotiate MoUs/DSAs with support from Legal/Compliance; ensure licensing terms, usage rights, and privacy are respected.
• Set SLAs for delivery, quality, and format with country partners; monitor adherence and escalate risks early.

• Track procurement budget and cost per dataset; propose cost-effective alternatives when needed.

• Data Systems & Integration

• Align datasets to the data warehouse schema; define data contracts and SLAs with Tech.

• Co-build/maintain dashboards for portfolio health, outcomes, and underwriting inputs (BI tools).
• Version control for data artifacts, queries, and calculations; maintain data dictionary.

Requirements

Educational Qualifications
Minimum Bachelor's Degree in project management, analytics, Economics, Statistics, Applied Economics
Technical Skills, Competencies, and Experience

• Bachelor's in Economics, Data Science, Statistics, Finance, or related field.
• 3–5 years in research/data analysis/credit modelling or adjacent roles.
• Proven experience cleaning, joining, and analysing education/employment datasets and building BI dashboards.
• Familiarity with income-based repayment/loan portfolios/credit scoring is advantageous.
• Experience across African markets preferred; strong written and verbal communication.
• Experience collaborating with country teams and external data owners (government, PEIs, research firms).
• Working knowledge of data licensing, privacy, and DPAs (GDPR/local equivalents) is a plus.

• Design, develop, and implement machine learning models and algorithms for predictive analytics, customer segmentation, and forecasting.
• Clean, transform, and preprocess large, complex datasets from various sources within the hospitality sector.
• Conduct exploratory data analysis to uncover insights and identify key drivers of customer behavior and business performance.
• Develop and maintain data pipelines for model training and deployment.
• Evaluate and optimize model performance using rigorous statistical methods.
• Collaborate with product teams to integrate data science solutions into client-facing applications.
• Communicate complex analytical findings and recommendations clearly and concisely to technical and non-technical audiences.
• Stay abreast of the latest advancements in AI, machine learning, and data science techniques.
• Mentor junior data scientists and contribute to the team's knowledge base.
• Contribute to the development of data strategy and roadmap.

• Master's or Ph.D. in Computer Science, Statistics, Data Science, Mathematics, or a related quantitative field.
• Minimum of 6 years of professional experience as a Data Scientist, with a significant focus on AI/ML applications.
• Proven expertise in statistical modeling, machine learning algorithms (e.g., regression, classification, clustering, deep learning), and data mining techniques.
• Proficiency in programming languages such as Python or R, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with SQL and database management.
• Familiarity with big data technologies (e.g., Spark, Hadoop) is a plus.
• Strong analytical and problem-solving skills.
• Excellent communication, presentation, and interpersonal skills, crucial for remote collaboration.
• Demonstrated ability to work independently, manage projects, and deliver results in a remote environment.
• Experience in the hospitality or travel industry is highly desirable.

• Design, develop, and implement sophisticated machine learning models and algorithms to solve complex business problems.
• Conduct exploratory data analysis to identify patterns, trends, and insights from large and complex datasets.
• Perform feature engineering, model selection, training, validation, and hyperparameter tuning.
• Develop predictive and prescriptive models for applications such as forecasting, anomaly detection, recommendation systems, and natural language processing.
• Evaluate model performance, interpret results, and communicate findings clearly to technical and non-technical stakeholders.
• Collaborate with software engineers to deploy machine learning models into production environments.
• Stay abreast of the latest research and advancements in machine learning, AI, and related fields.
• Design and conduct experiments to test hypotheses and validate model effectiveness.
• Develop and maintain robust data pipelines for model training and inference.
• Mentor junior data scientists and contribute to the team's technical growth.
• Ensure data quality, integrity, and privacy throughout the data science lifecycle.
• Write clean, well-documented, and reproducible code, primarily in Python or R.
• Contribute to the development of intellectual property through research and innovation.

• Ph.D. or Master's degree in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field with a focus on Machine Learning.
• Proven track record of conducting cutting-edge research and developing impactful machine learning models.
• Extensive experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Strong programming skills in Python or R, including experience with data manipulation libraries (e.g., Pandas, NumPy).
• Proficiency in SQL and experience working with various database systems.
• Deep understanding of statistical concepts, algorithms, and their practical application.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Superior communication and presentation skills, capable of explaining complex technical concepts to diverse audiences remotely.
• Ability to work independently, manage projects, and drive research initiatives from conception to completion.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) for machine learning is beneficial.

Our client is a multinational financial services firm, with a significant footprint.

Based in either Nairobi or Kuala Lumpur, this role will play a critical role in supporting their internal audit activities by providing insights through the analysis and correlation of data extracted from various systems across the business. Through advanced data analytics techniques, the role will enable the audit team to identify control failures, assess risks, and provide independent assurance to senior leadership that risks are being managed effectively and remain within the organization's approved risk appetite.

Key analytical activities include:

• Data profiling:
  Generating statistics to provide context and background on business processes.
• Reperformance testing:
  Re-performing calculations to validate transactions (e.g., payroll, claims), metrics from management information reports, and analytics models.
• Exception detection:
  Identifying instances where known restrictions, policies, or guidelines have not been followed.
• Anomaly detection:
  Detecting unusual or unexpected patterns of behaviour through data analysis.
• Root cause analysis:
  Identifying and quantifying the factors contributing to control failures and providing actionable insights.

The role also supports the internal audit leadership team in assessing the overall effectiveness of risk management practices.

Scope of the Role

As part of the regional audit resource pool, the Data Analytics Manager will be responsible for:

• Applying data analytics tools such as
  SQL
  and
  Python
  to support audit teams across the full audit lifecycle, from planning through closure.
• Detecting symptoms of control failures by analyzing operational data and providing insights to audit leads.
• Developing and maintaining
  continuous auditing dashboards
  using visualization tools such as
  Tableau
  or
  Power BI
  , providing clear visibility of control effectiveness across business units.
• Identifying trends and emerging themes that support the development of opinions on the organization's overall control environment.
• Supporting leadership in delivering continuous improvement initiatives and meeting stakeholder reporting requirements.
• Collaborating with internal teams, including
  IT
  and
  Compliance
  , to facilitate efficient data sourcing for audits and analytics initiatives.

Key Accountabilities

• Build and maintain strong working relationships with audit directors, internal audit leadership, and operational management teams.
• Deliver analytics outputs in line with internal methodologies and audit timelines.
• Provide audit leads with timely, accurate data-driven insights during fieldwork.
• Present audit findings to senior management, ensuring that appropriate responses are obtained for each identified issue.
• Manage analytics projects effectively, taking ownership of deliverables from start to finish.

Core Competencies

• Strong technical skills with
  SQL
  and
  Python (Pandas Library)
  for data analytics.
• Proficiency in visualization tools such as
  Tableau
  and/or
  Power BI
  .
• Advanced knowledge of
  Python
  /
  R
  for text analytics, NLP, predictive analytics, or machine learning is highly desirable.
• Excellent stakeholder management, communication, and presentation skills.

Knowledge & Experience

• Hands-on technical experience
  mining, cleaning, and analyzing data using databases and analytics tools.
• Strong working knowledge of SQL in a relational database environment.
• Solid experience using Python for data analysis and automation.
• Experience with machine learning and predictive analytics is advantageous.
• Background in
  financial services
  preferred, ideally with exposure to
  insurance
  or
  investment management
  sectors.
• Experience working within an
  audit
  or
  risk management
  environment is highly desirable.
• Exceptional attention to detail and logical problem-solving abilities.
• A collaborative team player with the ability to build and maintain professional respect within the team and across departments.

• Design, develop, and implement advanced machine learning models and algorithms.
• Analyze large and complex datasets to extract actionable insights and identify patterns.
• Perform feature engineering, data preprocessing, and model validation.
• Deploy machine learning models into production environments and monitor their performance.
• Collaborate with cross-functional teams to define problem statements and develop data-driven solutions.
• Conduct research on new AI techniques and technologies, and evaluate their potential applications.
• Communicate complex technical concepts and findings to stakeholders at all levels.
• Mentor junior data scientists and contribute to the team's technical growth.
• Stay current with the latest research and advancements in AI, machine learning, and data science.
• Contribute to the development of data strategy and best practices.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Minimum of 6 years of experience in data science, with a strong focus on AI and machine learning.
• Proven experience with various machine learning algorithms (e.g., regression, classification, clustering, deep learning).
• Proficiency in programming languages such as Python or R, and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, Azure, GCP).
• Strong understanding of statistical modeling, experimental design, and A/B testing.
• Excellent problem-solving, analytical, and critical thinking skills.
• Exceptional communication and presentation abilities.
• Demonstrated ability to work independently and lead projects in a remote setting.
• Specialization in NLP, computer vision, or reinforcement learning is a significant plus.

• Assist senior data scientists in collecting, cleaning, and pre-processing large datasets for analysis.
• Support the development and implementation of machine learning models and algorithms.
• Conduct exploratory data analysis to identify trends, patterns, and insights.
• Help in building and testing predictive models using Python and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Visualize data and model results for effective communication to technical and non-technical audiences.
• Collaborate with team members on data-driven projects and problem-solving.
• Participate in code reviews and contribute to maintaining high-quality code standards.
• Research and stay updated on the latest advancements in AI, ML, and data science.
• Document methodologies, findings, and project progress clearly.
• Assist in deploying machine learning models into production environments.
• Learn and apply statistical analysis techniques to interpret data.
• Contribute to team meetings and brainstorming sessions.

• Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Foundational understanding of machine learning concepts, algorithms, and statistical modeling.
• Proficiency in Python programming, including libraries such as Pandas, NumPy, scikit-learn, and a basic understanding of ML frameworks like TensorFlow or PyTorch.
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn).
• Basic knowledge of SQL for data querying.
• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills.
• Eagerness to learn, proactive attitude, and ability to work independently in a remote setting.
• Ability to work collaboratively within a virtual team environment.
• Passion for data science, artificial intelligence, and machine learning.
• Previous internship or project experience in data analysis or machine learning is a plus.

• Ph.D. or Master's degree in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• Minimum of 5 years of experience in data science, with a strong focus on AI/ML.
• Proven experience in developing and deploying machine learning models in production.
• Expertise in statistical modeling, hypothesis testing, and experimental design.
• Proficiency in programming languages such as Python or R, and relevant ML libraries (TensorFlow, PyTorch, Keras, scikit-learn).
• Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
• Strong understanding of data structures, algorithms, and software engineering best practices.
• Excellent problem-solving, analytical, and critical thinking skills.
• Effective communication and presentation skills to explain complex findings to diverse audiences.
• Ability to work independently and collaboratively in a remote team environment.

• Collect, clean, and organize large datasets from clinical trials, R&D, sales, and market research.
• Perform statistical analysis to identify trends, patterns, and insights related to drug performance and patient populations.
• Develop and maintain dashboards and reports to visualize key performance indicators for R&D, clinical operations, and commercial teams.
• Collaborate with cross-functional teams, including scientists, clinicians, and marketing specialists, to understand data requirements.
• Assist in the interpretation of clinical trial results and contribute to regulatory submission documentation.
• Identify opportunities for process improvements in data collection and analysis.
• Ensure data integrity, accuracy, and adherence to pharmaceutical industry standards and regulations.
• Stay updated on relevant scientific literature and industry best practices in data analysis.
• Translate complex data findings into clear, actionable recommendations for stakeholders.

• Bachelor's degree in Statistics, Bioinformatics, Data Science, Pharmacy, or a related quantitative field.
• Proven experience in data analysis, preferably within the pharmaceutical or healthcare industry.
• Proficiency in statistical software packages (e.g., R, SPSS, SAS) and data visualization tools (e.g., Tableau, Power BI).
• Strong understanding of clinical trial processes and pharmaceutical R&D lifecycles.
• Knowledge of regulatory requirements (e.g., FDA, EMA guidelines) is a plus.
• Excellent analytical skills and attention to detail.
• Strong problem-solving abilities and the capacity to work independently.
• Effective communication and interpersonal skills.
• Familiarity with database management and SQL.
• A passion for improving healthcare outcomes through data insights.