2,518 Remote Junior Data Scientist Predictive Analytics jobs in Kenya

• Assist in the development, implementation, and validation of machine learning models for predictive analytics.
• Clean, transform, and preprocess large, complex datasets to prepare them for analysis and modeling.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, and anomalies within datasets.
• Support the design and execution of A/B tests and other experiments to evaluate model performance and business impact.
• Visualize data and model results using various tools and techniques to communicate findings effectively.
• Collaborate with senior data scientists to understand project requirements and contribute to the overall analytical approach.
• Document methodologies, code, and findings thoroughly to ensure reproducibility and knowledge sharing.
• Stay up-to-date with the latest advancements in data science, machine learning, and statistical modeling.
• Participate in team meetings and contribute to discussions on data strategy and analytical approaches.
• Assist in the deployment and monitoring of predictive models in production environments.

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• Strong foundational knowledge of statistical concepts and machine learning algorithms (e.g., regression, classification, clustering).
• Proficiency in programming languages commonly used in data science, such as Python or R.
• Experience with data manipulation libraries (e.g., Pandas, NumPy) and machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Familiarity with SQL for database querying.
• Excellent problem-solving skills and a keen analytical mindset.
• Strong communication skills, with the ability to explain technical concepts to non-technical audiences.
• Ability to work independently, manage time effectively, and contribute as part of a remote team.
• A genuine passion for data and a desire to learn and grow in the field of data science.
• Previous internship or project experience in data analysis or machine learning is a plus.

• Lead the development and implementation of predictive models and machine learning algorithms to solve complex business problems.
• Design and execute advanced statistical analyses and experiments to extract actionable insights from large datasets.
• Collaborate with business stakeholders to understand their needs and translate them into data science projects.
• Mentor and guide junior data scientists, fostering best practices in modeling, validation, and deployment.
• Evaluate and select appropriate machine learning techniques, tools, and platforms.
• Develop and maintain robust data pipelines for model training and inference.
• Communicate complex analytical findings and recommendations clearly and concisely to both technical and non-technical audiences.
• Stay abreast of the latest research and advancements in data science, machine learning, and artificial intelligence.
• Contribute to the development of data strategy and roadmap for the organization.
• Ensure the ethical and responsible use of data in all analytical endeavors.
• Collaborate with data engineers and software developers to deploy models into production environments.

• Ph.D. or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field.
• Minimum of 7 years of progressive experience in data science, with a strong track record in predictive modeling and machine learning.
• Expertise in statistical modeling, machine learning algorithms (e.g., regression, classification, clustering, deep learning), and experimental design.
• Proficiency in programming languages commonly used in data science, such as Python or R, and associated libraries (e.g., scikit-learn, TensorFlow, PyTorch, Pandas).
• Experience with big data technologies (e.g., Spark, Hadoop) and database querying (SQL).
• Strong understanding of data visualization techniques and tools (e.g., Matplotlib, Seaborn, Tableau).
• Excellent analytical, problem-solving, and critical thinking skills.
• Exceptional communication and leadership skills, with the ability to guide and inspire a remote team.
• Proven ability to manage complex data science projects from conception to completion.

• Design, develop, and implement sophisticated predictive models using machine learning algorithms and statistical techniques.
• Analyze large and complex datasets to identify patterns, trends, and correlations relevant to research objectives.
• Develop and validate models for forecasting, classification, regression, and anomaly detection.
• Work with stakeholders to understand business needs and translate them into data science problems.
• Clean, preprocess, and transform data to ensure its quality and suitability for modeling.
• Evaluate model performance and iterate on approaches to improve accuracy and effectiveness.
• Communicate complex findings and insights clearly and concisely through visualizations and reports to both technical and non-technical audiences.
• Collaborate with engineers to deploy models into production environments.
• Stay abreast of the latest advancements in data science, machine learning, and artificial intelligence.
• Mentor junior data scientists and contribute to the team's technical growth.

• Ph.D. or Master's degree in Statistics, Computer Science, Mathematics, Physics, or a related quantitative field.
• Minimum of 7 years of experience in data science, with a strong focus on predictive modeling and machine learning.
• Proficiency in programming languages such as Python (with libraries like scikit-learn, TensorFlow, PyTorch, Pandas) or R.
• Extensive experience with various machine learning algorithms (e.g., linear regression, logistic regression, decision trees, random forests, gradient boosting, SVMs, neural networks).
• Strong understanding of statistical concepts, experimental design, and hypothesis testing.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI).
• Familiarity with big data technologies (e.g., Spark, Hadoop) and SQL.
• Excellent problem-solving skills and a passion for tackling challenging analytical problems.
• Strong communication and presentation skills, with the ability to explain technical concepts to diverse audiences.
• Experience in scientific research or R&D environments is a significant advantage.

• Assist in the collection, cleaning, and preprocessing of diverse datasets from various sources.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
• Develop and implement basic machine learning models under the guidance of senior team members.
• Visualize data and model results using tools such as Python libraries (Matplotlib, Seaborn) or Tableau.
• Collaborate with data scientists and engineers on feature engineering and model selection.
• Document methodologies, findings, and code for reproducibility and knowledge sharing.
• Participate in regular team meetings and contribute to project discussions.
• Learn and apply new data analysis techniques and tools relevant to AI/ML.
• Assist in the testing and validation of AI/ML models.
• Contribute to the overall data strategy and innovation efforts.

• Recent graduate with a Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field.
• Foundational understanding of statistical concepts and data analysis techniques.
• Basic programming skills in Python or R, including experience with relevant data manipulation and analysis libraries (e.g., Pandas, NumPy).
• Familiarity with machine learning concepts and algorithms is highly desirable.
• Exposure to data visualization tools.
• Strong analytical and problem-solving abilities.
• Excellent communication and teamwork skills, essential for remote collaboration.
• Proactive attitude and eagerness to learn and take on new challenges.
• Self-motivated and able to manage tasks effectively in a remote environment.
• A genuine interest in AI and Machine Learning.

About the Role:

Help shape the future of AI We're seeking detail-oriented freelancers to join a global AI data annotation project. As an AI Data Annotation Specialist, you'll annotate, label, and curate data that powers advanced machine learning models. Your work—tagging images, labeling text, or collecting documents—will directly impact the accuracy and performance of AI systems used worldwide.

Key Responsibilities:

• Annotate & Label: Tag, categorize, and review text, images, and documents.
• Collect Data: Gather and organize high-quality datasets for AI training.
• Ensure Quality: Review and validate data for precision and consistency.
• Collaborate Globally: Work with an international team to hit project milestones.

Basic Requirements:

• Strong attention to detail and ability to follow precise instructions.
• Reliable internet connection and ability to work independently.
• Prior experience in data annotation or labeling is a plus but not required.
• Basic understanding of AI or machine learning concepts is advantageous.
• Strong proficiency in English for clear communication and reporting.

• Assist in collecting, cleaning, and transforming large datasets from various sources.
• Perform exploratory data analysis (EDA) to identify patterns, trends, and anomalies.
• Support the development and testing of machine learning models and algorithms under supervision.
• Create compelling data visualizations and dashboards to communicate findings to stakeholders.
• Contribute to the documentation of data processes, methodologies, and code.
• Collaborate with senior data scientists and analysts on ongoing projects.
• Learn and apply statistical concepts and data mining techniques.
• Participate in team meetings and contribute ideas for data-driven solutions.
• Assist in preparing reports and presentations summarizing analytical results.
• Develop proficiency in data analysis tools and programming languages relevant to data science.

• Recent graduate with a Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
• Strong understanding of statistical concepts and analytical techniques.
• Basic knowledge of programming languages commonly used in data science (e.g., Python, R).
• Familiarity with data visualization tools (e.g., Tableau, Power BI) is a plus.
• Excellent analytical and problem-solving skills.
• Strong attention to detail and ability to work with large datasets.
• Good communication and interpersonal skills, with the ability to work effectively in a remote team.
• Eagerness to learn and a proactive attitude.
• Ability to manage tasks and time effectively.
• Passion for data and uncovering insights.

• Assist in collecting, cleaning, and organizing large datasets from various sources.
• Perform basic data analysis and generate reports under the guidance of senior analysts.
• Develop and maintain data visualizations to help communicate insights effectively.
• Support the development of dashboards and reports using business intelligence tools.
• Conduct research on industry trends and competitive landscape to inform analytical projects.
• Collaborate with team members on ad-hoc data analysis requests.
• Learn and apply statistical methods and data modeling techniques.
• Participate in team meetings and contribute ideas for data-driven solutions.
• Assist in documenting data sources, methodologies, and findings.
• Gain exposure to programming languages commonly used in data analysis, such as Python or R.

• Recent graduate with a Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, Data Science, or a related discipline.
• Strong analytical and problem-solving skills.
• Proficiency in Microsoft Excel for data manipulation and analysis.
• Basic understanding of statistical concepts and data analysis principles.
• Familiarity with data visualization tools (e.g., Tableau, Power BI) is a plus.
• Exposure to programming languages like Python or R is highly desirable.
• Excellent written and verbal communication skills.
• Ability to work independently and manage tasks effectively in a remote setting.
• Eagerness to learn and a strong work ethic.
• Enthusiasm for data and analytics.