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The Junior Data Scientist will support data-driven decision-making by collecting, cleaning, and analyzing datasets under the mentorship of senior team members. They will assist in developing and evaluating machine learning models, performing statistical analyses, and creating visualizations to communicate insights to stakeholders. This role is ideal for someone with 2–3 years of hands-on experience in Python programming and foundational AI research, eager to grow their skills within a collaborative data science environment.
- Gather, clean, and preprocess large datasets, handling missing values and outliers to ensure data quality.
- Extract and engineer features from raw data to improve model performance, working closely with senior data scientists to identify relevant variables.
- Assist in implementing, testing, and tuning machine learning algorithms (e.g., regression, classification, clustering) using Python libraries such as scikit-learn and TensorFlow.
- Perform hypothesis testing, A/B experiments, and exploratory data analysis to uncover trends and inform business strategies.
- Create clear, impactful visualizations and reports using tools like Matplotlib, Seaborn, or Tableau to communicate findings to both technical and non-technical audiences.
- Work collaboratively within agile teams, document methodologies in reproducible notebooks, and participate in code reviews to maintain high standards.
- 2–3 years in data science or related roles, with demonstrable project experience in data manipulation and basic ML model development.
- Proficient in Python, including libraries such as pandas, NumPy, scikit-learn, and TensorFlow or PyTorch.
- Solid understanding of statistical concepts (e.g., regression, classification, clustering, hypothesis testing) and their practical application.
- Familiarity with SQL for querying relational databases and experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
- Strong analytical thinking, clear communication skills, and the ability to explain technical results to diverse audiences.
- Bachelor’s or Master’s in Computer Science, Statistics, Mathematics, Data Science, or a related field.
- Experience with additional ML frameworks (e.g., Keras, XGBoost) and version-control/model-tracking tools (e.g., Git, MLflow).
- Exposure to cloud-based ML services (e.g., AWS SageMaker, GCP AI Platform, Azure ML).
- Knowledge of natural language processing or computer vision techniques.
- Contributions to open-source data science projects or participation in relevant research publications.
