Engineering

Data Scientist

Remote
Work Type: Full Time

 

Data Scientist Job Description

Company: Codvo

Job Title: Data Scientist

Experience: 4+ years

About the Role

We are seeking a highly skilled Data Scientist with a strong background in Machine Learning and Deep Learning to join our team. The ideal candidate will have experience applying advanced ML techniques to solve real-world business and customer problems, and will be comfortable working with large, complex datasets.

Key Responsibilities

•            Design and implement end-to-end ML pipelines, including data ingestion, cleaning, feature engineering, model training, deployment, and monitoring.

•            Develop and experiment with Deep Neural Network architectures for various business use cases.

•            Translate ambiguous business requirements into clear technical solutions and communicate insights effectively to both technical and non-technical stakeholders.

•            Work with large datasets for acquisition, preprocessing, and analysis.

•            Ensure code quality and maintainability by following software engineering best practices.

•            Collaborate with cross-functional teams to integrate ML solutions into production systems.

•            Utilize CI/CD, version control (Git), and automated testing in ML projects.

Required Skills & Qualifications

•            4+ years of experience applying Machine Learning to real-world business/customer problems.

•            Strong programming skills in Python and experience with ML libraries such as scikit-learn, pandas, numpy, TensorFlow, PyTorch, and Keras.

•            Solid foundations in Deep Learning with hands-on experience in designing and deploying neural network architectures.

•            Experience building robust ML pipelines and deploying models in production environments.

•            Strong understanding of software engineering principles for maintainable and testable codebases.

•            Ability to communicate technical concepts clearly to diverse audiences.

•            Experience with CI/CD, Git, and automated testing in ML workflows.

Nice to Have

•            Exposure to cloud platforms (AWS, GCP, Azure) for ML deployment.

•            Familiarity with MLOps tools and frameworks.

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