KEY RESPONSIBILITIES
- Collaborate with cross-functional teams—including data scientists, software engineers, and product managers—to define machine learning problems and objectives.
- Research, design, and implement machine learning algorithms and models, such as supervised, unsupervised, deep learning, and reinforcement learning techniques.
- Analyze and preprocess large-scale datasets for training and evaluation purposes.
- Train, test, and optimize ML models to ensure accuracy, scalability, and high performance.
- Deploy ML models into production environments using cloud platforms and MLOps best practices.
- Monitor and assess model performance over time to maintain reliability and robustness.
- Document methodologies, findings, and results to communicate insights effectively to stakeholders.
QUALIFICATIONS, EXPERIENCE & SKILLS
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (recent graduates or soon-to-be graduates within the last 12 months).
- Proficient in Python or a similar programming language, with hands-on experience using ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Solid understanding of linear algebra, probability, statistics, and optimization methods.
- Familiarity with key machine learning algorithms (e.g., decision trees, SVMs, neural networks) and concepts including feature engineering, overfitting, and regularization.
- Experience working with both structured and unstructured data using tools such as Pandas, SQL, or Spark.
- Strong analytical and problem-solving abilities to tackle complex ML challenges.
- Excellent communication and teamwork skills for effective collaboration within diverse teams.
ADDITIONAL SKILLS (PREFERRED)
- Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools like MLflow or Kubeflow.
- Knowledge of distributed computing or big data technologies such as Hadoop or Apache Spark.
- Previous internships, academic research, or projects demonstrating practical ML expertise.
- Familiarity with deployment and containerization tools like Docker and Kubernetes.
Equal Opportunity Statement
DP World is dedicated to Equal Employment Opportunity (EEO) principles. We believe diversity strengthens our organization, and we make hiring decisions based solely on experience and skills. We encourage applications from all individuals regardless of age, gender, disability, race, religion, or belief.
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