Junior Machine Learning Engineer at Pineapple

The role of a Junior Machine Learning Engineer within the short-term motor insurance industry is centred around supporting the Data Science and Machine Learning team in designing, developing, and deploying machine learning models, ensuring high performance and alignment with business goals.

Responsibilities:

Model Development:

• Assist in the design and development of machine learning algorithms and models.
• Experiment with new algorithms and techniques in machine learning and deep learning.
• Participate in prototyping and validation of models to meet business objectives.

Data Management:

• Collaborate with the data engineering team to understand and utilize company data sources.
• Preprocess and clean data to ensure optimal performance of machine learning models.
• Explore datasets to understand patterns, anomalies, and data quality.

Implementation & Deployment:

• Work closely with software engineers to integrate machine learning models into products, services, and workflows.
• Monitor model performance and accuracy in real-world applications.
• Assist in model updating and maintenance procedures.

Collaboration & Communication:

• Collaborate with interdisciplinary teams, including data engineers, software developers, and business analysts.
• Communicate findings, ideas, and progress effectively to both technical and non-technical stakeholders.
• Continuous Learning:
• Stay updated with the latest trends, techniques, and best practices in machine learning.
• Participate in internal and external training sessions, workshops, and conferences.

Qualifications:

Education:

Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. A Master's degree is a plus.

Technical Skills:

• Proficiency in Python, with experience using libraries like TensorFlow, Keras, PyTorch, and scikit-learn.
• Familiarity with SQL and relational databases.
• Basic understanding of cloud platforms like AWS, Google Cloud, or Azure.
• Experience with version control systems, preferably Git.

Experience:

• 0-2 years of experience in machine learning, data analysis, or a related field.
• Experience in solving analytical problems using quantitative approaches.
• Experience with deep learning is a plus.

Soft Skills:

• Strong analytical and problem-solving abilities.
• Good communication skills, both written and verbal.
• Ability to work in a fast-paced, collaborative environment.
• Curiosity and eagerness to learn.

Preferred (but not required):

• Exposure to big data technologies like Spark, Hadoop.
• Experience with machine learning deployment tools such as Docker, Kubernetes, or MLflow.