Intern Data Scientist/Statistician 228 views

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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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 datasets, analytical workflows and the integration of statistical or machine-learning outputs into practical solutions.
• Participate in meetings, demonstrations and technical review sessions.

EXPECTED OUTPUTS

Depending on the duration of the internship and the projects assigned, the intern will be expected to contribute to the following outputs:

• Contributions to cleaned, well-documented analysis-ready datasets and reproducible analytical scripts.
• Statistical analyses, model outputs and interpretations prepared and reviewed under appropriate supervision.
• Contributions, under supervision, to prototype statistical, machine-learning, NLP or other analytical components.
• Clear tables, charts, dashboards or analytical reports.
• Version-controlled code with appropriate documentation.
• Regular progress updates and an end-of-internship presentation or portfolio.

PERSON SPECIFICATION

ESSENTIAL

Education

• Final-year students or recent graduates in Statistics, Data Science, Mathematics, Biostatistics, Computer Science or another quantitative discipline.

Knowledge and Skills

• Working knowledge of R or Python gained through coursework, a dissertation, personal projects, an internship or similar experience.

• Good foundation in quantitative methods, including descriptive statistics and probability, with some understanding of statistical inference, regression or related analytical methods.

• Some experience cleaning, exploring and visualising data using real or academic datasets.

• Introductory understanding of machine learning and model evaluation, with willingness to develop these skills further.

• Ability to break down practical questions into clear analytical tasks and explain findings logically.

• Good written and verbal communication skills.

• Commitment to accurate, transparent and reproducible analysis and responsible handling of data.

Personal Attributes

• Curious, analytical and eager to learn.

• Organised and able to manage assigned tasks and deadlines.

• Careful and attentive to data quality and detail.

• Professional, reliable and respectful of confidentiality.

• Comfortable asking questions, acknowledging uncertainty and seeking clarification when needed.

• Motivated to use data to solve practical problems.

DESIRABLE

• Demonstrated interest or exposure to Natural Language Processing (NLP), deep learning, speech or audio processing, evidenced through coursework, a dissertation, academic work, personal projects or self-directed learning.

• Exposure to machine-learning tools such as scikit-learn, PyTorch, TensorFlow or similar libraries.

• Interest in or exposure to text, speech, audio, multilingual or low-resource language data, or other forms of unstructured data.

• Familiarity with one or more supporting tools such as Structured Query Language (SQL), Git/GitHub or Application Programming Interfaces (APIs).

• Exposure to business intelligence and visualisation tools such as Microsoft Power BI, Shiny, Streamlit, Plotly or similar application tools.

• Interest in how analytical or machine-learning models can be integrated into real-world applications would be an advantage.

• Candidates are not expected to meet every desirable criterion; evidence of strong learning ability and relevant project work will be considered.

PERFORMANCE AND LEARNING ASSESSMENT

Performance will be reviewed through regular supervision and feedback. Assessment will consider:

Assessment Area

What Will Be Considered

Delivery

Completion of agreed analytical tasks and outputs within reasonable timelines.

Analytical quality

Accuracy, appropriateness, reproducibility and interpretation of analyses and models.

Learning and improvement

Ability to apply feedback and develop new skills during the placement.

Problem solving

Ability to investigate data or modelling issues and propose practical solutions.

Documentation

Clarity and completeness of code, analysis notes and technical documentation.

Teamwork and communication

Professional engagement, progress reporting and clear communication of findings.

Reliability

Attendance, punctuality, ownership and adherence to company procedures.

WHAT THE INTERN WILL GAIN

Statistical Analysis Experience

Hands-on experience analysing real datasets and interpreting results.

Machine Learning and AI

Opportunities to develop and evaluate predictive models under supervision.

NLP and Unstructured Data

Opportunities to gain exposure to methods for text, speech, audio and other unstructured data.

Mentorship

Mentorship and feedback from experienced data professionals and project leads.

Real-World Data Projects

Exposure to health research, business analytics and other data-driven projects.

Professional Growth

Improved confidence in reproducible analysis, modelling and communication.

TERMS AND CONDITIONS

• The internship is full-time and based at The Hub, Kairaba Avenue, unless an alternative arrangement is agreed.
• The internship will normally run for three to six months.
• The intern must comply with AcroMetrics policies, including confidentiality, information security, data protection and acceptable use of company systems.
• Any stipend or allowance will be communicated separately in the offer letter.
• Successful completion of the internship does not guarantee permanent employment; however, strong performers may be considered for future opportunities where available.

APPLICATION AND CONTACT INFORMATION

APPLICATION DEADLINE: On a rolling basis!

Send application documents to, email: apply@gamjobs.com
Website: www.acro-metrics.com
LinkedIn: AcroMetrics Consultancy Company Ltd.
Facebook: @acro.metrics

Join AcroMetrics and contribute to practical, reliable and scalable data solutions that support better decisions across health research and data-driven organisations.