Jr. ML Developer

About the Role

As a Junior Machine Learning Developer, you will play a critical role in supporting the design, development, and deployment of machine learning models that drive data-driven decision-making across the organization. You will collaborate closely with Sr. Data Engineer and Director of Technology to implement scalable algorithms and contribute to the continuous improvement of existing ML systems. Your work will directly impact the efficiency and accuracy of predictive analytics, enabling the company to leverage data insights for strategic advantage. This role offers a unique opportunity to grow your expertise in machine learning within a dynamic and innovative environment. You will be involved in the entire ML lifecycle, from data preprocessing and feature engineering to model evaluation and deployment.

Key Responsibilities

• Assist in the development and implementation of machine learning models and algorithms under the guidance of senior team members.
• Perform data cleaning, preprocessing, and feature extraction to prepare datasets for modeling.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Conduct experiments to evaluate model performance and optimize parameters for improved accuracy and efficiency.
• Support the deployment and monitoring of ML models in production environments to ensure reliability and scalability.

Requirements & Tech Stack

• Minimum Qualifications:
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Basic understanding of machine learning concepts and algorithms such as regression, classification, clustering, and neural networks.
• Proficiency in programming languages commonly used in ML, such as Python or R.
• Familiarity with ML libraries and frameworks like scikit-learn, TensorFlow, or PyTorch.
• Experience with data manipulation and analysis using tools such as pandas and NumPy.
• Preferred Qualifications:
• Internship or project experience involving real-world machine learning applications.
• Knowledge of cloud platforms and services related to ML deployment, such as AWS, Azure, or Google Cloud.
• Understanding of software development best practices including version control (e.g., Git) and testing.
• Experience with SQL and working with large datasets.
• Exposure to deep learning techniques and natural language processing.