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.
