Data Scientist at Busha Digital LTD

As a Data Scientist at Busha, you will be at the core of our strategic initiatives. You will tackle complex business problems by applying advanced analytical techniques, designing machine learning models, and translating raw data into actionable insights that will shape product development, operational efficiency, and customer experience.

Key Responsibilities

• Problem Formulation: Collaborate with product, engineering, and marketing teams to identify business challenges and translate them into well-defined data science projects.
• Data Exploration & Preparation: Extract, clean, and preprocess large, complex datasets from various sources (e.g., SQL/NoSQL databases, APIs, logs). Perform rigorous exploratory data analysis (EDA) to uncover trends and patterns.
• Modeling & Validation:
  • Develop, train, and validate predictive models using machine learning algorithms (e.g., regression, classification, clustering, time series forecasting).
  • Apply appropriate statistical methods for inference and hypothesis testing (e.g., t-tests, ANOVA, chi-squared (χ2) tests).
• Implementation & Deployment: Work with Data and software Engineers to deploy models into production environments. Monitor model performance and conduct iterative improvements.
• Experimentation: Design, execute, and analyze A/B tests to measure the impact of new features, algorithms, and business strategies.
• Communication: Visualize and communicate complex findings, model results, and strategic recommendations to both technical and non-technical stakeholders in a clear and compelling manner.

The ideal employee must have:

• Education: Bachelor’s or Master’s degree in a quantitative field like Computer Science, Statistics, Mathematics, Physics, Engineering, or a related discipline.
• Experience: 3+ years of hands-on experience in a data science, machine learning, or quantitative analysis role.
• Technical Skills:
  • High proficiency in Python and core data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn).
  • Strong command of SQL for complex data querying and manipulation.
  • Solid theoretical and practical understanding of machine learning concepts and statistical modeling.
  • Hands-on experience with data warehousing solutions.
• Soft Skills: Proven problem-solving ability, business acumen, and strong communication skills.

Nice to Have:

• Experience with cloud platforms (GCP, AWS, or Azure) and their associated ML services.
• Familiarity with big data technologies (e.g., Spark, Dask).
• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Knowledge of MLOps principles and tools (e.g., MLflow, Kubeflow, Docker).
• Experience with data visualization tools (e.g., Metabase, Apache Superset, Tableau, Power BI, Looker).