Intern Data Scientist/Statistician

Administrative - Business development - Communication & Public Relations - Data Management - IT / Engineering - Monitoring and Evaluation - Operations - Other Job Overview INTERNSHIP OPPORTUNITY Statistical Analysis, Machine Learning and Applied Artificial Intelligence (AI) DURATION: 3-6 Months LOCATION: The Hub, Kairaba Avenue, The Gambia REPORTING TO: Assigned AcroMetrics Supervisor DEPARTMENT: Data Science & Analytics ENGAGEMENT TYPE: Internship STIPEND: Any stipend or allowance will be communicated separately in the offer letter. EMPLOYMENT OPPORTUNITY: Strong performers may be considered for future opportunities where available. ROLE PURPOSE The Intern Data Scientist/Statistician will support the analysis, modelling and interpretation of data and contribute to practical data-driven solutions. The internship combines applied statistics and data science, with opportunities to contribute to research analysis, machine learning and emerging Artificial Intelligence (AI) applications. Under supervision, the intern will develop practical experience while working closely with data managers, developers and other project staff on research datasets, analytical workflows and multidisciplinary projects. ABOUT ACROMETRICS AcroMetrics Consultancy Company Ltd. is a data and technology consultancy developing practical, scalable solutions for health research, public health and other data-driven sectors. Our work includes statistical consultancy, data management, analytics, software development and applied artificial intelligence. We are seeking a motivated Intern Data Scientist/Statistician with strong quantitative foundations, curiosity and a willingness to learn. The internship is designed for a final-year student or recent graduate who wants to develop practical data science and statistical skills while contributing to real projects. KEY RESPONSIBILITIES Statistical Analysis and Data Preparation Clean, validate and prepare datasets for analysis and modelling. Conduct exploratory analysis and produce clear descriptive summaries. Support statistical analyses, including regression and hypothesis tests, under guidance. Build reproducible analytical workflows using R or Python. Present findings through clear tables, charts and written summaries. Machine Learning and Predictive Modelling Support the development and evaluation of supervised and unsupervised machine-learning models under guidance. Assist with feature engineering, data splitting, cross-validation and model tuning where appropriate. Compare model performance using suitable metrics and simple baseline approaches. Document model assumptions, results, limitations and reproducible code. Applied AI and Unstructured Data Support, under supervision, analytical work involving text, speech, audio and other unstructured data. Assist with preparing unstructured data for analysis and modelling using appropriate preprocessing methods. Gain practical exposure to Natural Language Processing (NLP), deep learning and related methods through guided project work. Review model errors, limitations and performance across relevant groups, and discuss opportunities for improvement. Data Tools, Visualisation and Integration Use R or Python and, where appropriate, Structured Query Language (SQL) and relevant statistical or machine-learning packages. Develop dashboards, visualisations and analytical reports. Use Git and shared repositories for version control and collaboration. Support the integration of analytical outputs into applications and workflows in collaboration with developers and data managers. Documentation and Teamwork Maintain clear analysis notes, code documentation and model records. Apply confidentiality, data protection and responsible AI practices. Provide regular progress updates and raise data or modelling issues early. Work collaboratively and respond constructively to feedback. Collaborate with data managers and developers on research dataset…