Data Science Engineer

Responsibilities

• Research, design, and implement machine learning models and algorithms to solve business problems.
• Develop and train models using techniques such as supervised and unsupervised learning, deep learning, natural language processing (NLP), and computer vision.
• Perform data pre-processing, feature engineering, and model evaluation.
• Optimize models for performance, scalability, and efficiency.

• Work with large datasets to extract meaningful insights and patterns.
• Collaborate with data engineers to ensure data quality, availability, and consistency.
• Conduct exploratory data analysis (EDA) to understand data characteristics and inform modelling decisions.
• Utilize statistical methods and tools for data analysis and hypothesis testing.

• Deploy machine learning models to production environments, considering scalability and reliability.
• Develop APIs and integrations to enable seamless interaction between models and other systems.
• Monitor model performance in production, diagnose issues, and implement improvements.
• Collaborate with software engineers to integrate ML components into larger systems.

• Stay up-to-date with the latest advancements in AI/ML technologies and research.
• Experiment with new algorithms, frameworks, and tools to improve model performance.
• Propose and lead initiatives for exploring AI/ML applications in new areas.

• Work closely with product managers, data scientists, software engineers, and stakeholders to define project requirements and deliver solutions.
• Communicate technical concepts and findings to non-technical stakeholders in a clear and understandable manner.
• Participate in Agile development processes, including sprint planning, reviews, and retrospectives.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related field (or equivalent work experience); Master's or Ph.D. preferred.
• Proven experience in developing and deploying machine learning models in a production environment.
• Strong programming skills in languages such as Python, R, or Java.
• Proficiency with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit- learn).
• Experience with data pre-processing, feature engineering, and model evaluation.
• Solid understanding of machine learning algorithms and techniques (supervised, unsupervised, deep learning, etc.).
• Knowledge of cloud platforms (such as AWS, Azure, Google Cloud) and their AI/ML services.
• Familiarity with version control systems (Git) and Agile development methodologies.
• Excellent problem-solving skills and ability to think creatively.
• Strong communication skills and ability to work in a collaborative team environment.