• Collaborate with cross-functional teams (e.g., data scientists, software engineers, product managers) to define ML problems and objectives.
• Research, design, and implement machine learning algorithms and models (e.g., supervised, unsupervised, deep learning, reinforcement learning).
• Analyse and preprocess large-scale datasets for training and evaluation.
• Train, test, and optimize ML models for accuracy, scalability, and performance.
• Deploy ML models in production using cloud platforms and/or MLOps best practices.
• Monitor and evaluate model performance over time, ensuring reliability and robustness.
• Document findings, methodologies, and results to share insights with stakeholders.

QUALIFICATIONS, EXPERIENCE AND SKILLS

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (graduation within the last 12 months or upcoming).
• Proficiency in Python or a similar language, with experience in frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Strong foundation in linear algebra, probability, statistics, and optimization techniques.
• Familiarity with machine learning algorithms (e.g., decision trees, SVMs, neural networks) and concepts like feature engineering, overfitting, and regularization.
• Hands-on experience working with structured and unstructured data using tools like Pandas, SQL, or Spark.
• Ability to think critically and apply your knowledge to solve complex ML problems.
• Strong communication and collaboration skills to work effectively in diverse teams.

Additional Skills (Good to have)

• Experience with cloud platforms (e.g., AWS, Azure, GCP) and MLOps tools (e.g., MLflow, Kubeflow).
• Knowledge of distributed computing or big data technologies (e.g., Hadoop, Apache Spark).
• Previous internships, academic research, or projects showcasing your ML skills.
• Familiarity with deployment frameworks like Docker and Kubernetes.