- 경험
- Up to 5 yrs
- 샐러리
- —
- 채용 공고
- 1
- 게시됨
- 5시간 전
- Work mode
- 사무실에서
- Eligibility
- Candidates with 0–5 years of professional or project-based experience in ML or data science are encouraged to apply. Internship, capstone, and Kaggle competition experience are also relevant.
- Resume
- Required to apply
Where you'll work
직무 설명
Role overview
This position is a practical entry point into machine learning, designed for someone eager to learn quickly and contribute to production systems from the start. You’ll work closely with senior engineers on live ML initiatives, help build features, run experiments, and support model deployment while receiving ongoing guidance and mentorship.
What you’ll do
- Support the creation and refinement of machine learning models with supervision.
- Develop readable, testable Python code for preparing data, engineering features, and checking model performance.
- Investigate datasets using exploratory data analysis to identify useful patterns and data issues.
- Carry out offline and A/B testing, then document and share the results.
- Maintain current ML pipelines by resolving defects and improving efficiency.
- Work with data engineers to obtain, clean, and transform training data.
- Take part in code reviews and sprint meetings as part of the delivery process.
What we’re looking for
The ideal candidate has a strong desire to learn, execute, and develop under close mentorship. You should bring 0–5 years of experience from professional work or projects in machine learning or data science, along with solid Python skills and a practical understanding of core ML methods.
Skills and tools
Key technical areas include Python, NumPy, Pandas, scikit-learn, and at least one deep learning framework such as PyTorch or TensorFlow. You should understand regression, classification, and evaluation metrics, and be comfortable using SQL for database queries, Jupyter Notebooks for analysis, Git for version control, and collaborative development practices.
Nice to have
Relevant bonus experience includes internships, capstone projects, or Kaggle competitions. Exposure to cloud environments like AWS, GCP, or Azure is also helpful, as is familiarity with experiment tracking platforms such as MLflow or Weights & Biases.