Junior Machine Learning Developer
We are a forward-thinking technology innovator operating at the intersection of data science and practical application. Our distributed team is committed to solving complex challenges across sectors like finance, healthcare, and e-commerce by leveraging cutting-edge artificial intelligence. We foster a culture of continuous learning and collaboration, empowering our team members to push the boundaries of what's possible and grow their expertise in a supportive, results-driven environment.
Key Responsibilities
- Develop, train, and deploy machine learning models using modern frameworks like TensorFlow and PyTorch.
- Translate ambiguous business problems into concrete machine learning challenges, from conceptualization all the way to deployment.
- Perform comprehensive data preprocessing and feature engineering on large, complex datasets to prepare them for model training.
- Code and implement scalable, efficient ML algorithms in Python, adhering to software development best practices and maintaining clean, reusable code.
- Conduct rigorous testing and validation to ensure model accuracy, robustness, and performance... and then find ways to improve it.
- Debug and resolve intricate issues within the ML pipeline, from data ingestion to model serving.
- Collaborate closely with data scientists, software engineers, and product managers in a dynamic, agile environment.
- Document all processes, models, and code meticulously for knowledge sharing and future maintenance (a crucial, often overlooked skill).
- Optimize model performance and computational efficiency for real-world, high-stakes applications.
- Stay abreast of the latest advancements in machine learning and artificial intelligence to propose and implement novel solutions.
What You Bring to the Table
- A Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
- Demonstrable proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn).
- Hands-on experience with at least one major deep learning framework, such as TensorFlow or PyTorch, through academic projects, internships, or personal projects.
- A solid theoretical understanding of fundamental machine learning concepts—including regression, classification, clustering, and neural networks.
- Exceptional problem-solving and analytical skills, with a keen ability to deconstruct complex problems into manageable components.
- Strong communication skills, capable of explaining technical concepts to both technical and non-technical stakeholders with clarity and confidence.
- A foundational knowledge of version control systems, particularly Git.
Preferred Qualifications That Will Make You Stand Out
- A Master’s degree in a relevant field.
- Experience with cloud computing platforms (AWS, Azure, or GCP) and their ML services (e.g., SageMaker, Azure Machine Learning).
- Knowledge of SQL and experience working with relational or non-relational databases.
- Familiarity with containerization technologies like Docker for creating reproducible environments.
- Experience building or consuming APIs.
- A portfolio of personal or academic projects available on GitHub that showcases your practical ML skills and passion for the field.