Responsibilities ● Assist in collecting, cleaning, and preprocessing structured and unstructured datasets for model training and evaluation. ● Perform exploratory data analysis (EDA) to uncover patterns, trends, and potential data issues. ● Support the development and implementation of machine learning models under the guidance of senior team members. ● Run experiments to evaluate model performance, document results, and iterate on improvements. ● Create clear and effective visualizations to communicate insights and model results to technical and non-technical stakeholders. ● Contribute to documentation of workflows, methodologies, and deployment procedures to ensure reproducibility and operational stability. ● Participate in the deployment, versioning, and basic monitoring of models in production environments. ● Continuously learn and stay updated with developments in data science, machine learning, and MLOps practices.
Qualifications ● Bachelor’s degree in Computer Science, Data Science, or a related field.● 0–2 years of experience in data science, machine learning, or a related domain (internships and academic projects acceptable). ● Proficiency in Python and core data science libraries (e.g., NumPy, Pandas, Scikit-learn). ● Basic understanding of statistical concepts, probability, and machine learning algorithms. ● Knowledge of data preprocessing, feature engineering, and model evaluation techniques. ● Experience with data visualization libraries such as Matplotlib and Seaborn. ● Familiarity with SQL and database fundamentals. ● Strong analytical and problem-solving skills. ● Exposure to model deployment and automation via competitions or personal projects is a plus. ● Familiarity with cloud platforms (AWS, GCP, or Azure) and their ML services.
Technical Skills ● Programming: Python (required) ● Libraries/Frameworks: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn ● Machine Learning: Basic understanding of supervised/unsupervised learning, regression, classification, clustering, evaluation ● Data Manipulation: Cleaning, preprocessing, feature engineering ● Databases: Basic SQL ● Version Control: Git ● MLOps/DevOps: Basic understanding of CI/CD, model versioning, and monitoring. Familiarity with Docker and Kubernetes is a plus.
Soft Skills ● Strong communication skills with the ability to explain technical concepts clearly ● Keen interest in the operational aspects of machine learning ● High attention to detail and strong organizational abilities ● Team-oriented with collaborative work ethic ● Proactive mindset with a drive to improve workflows ● Willingness to learn emerging technologies and MLOps tools
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