Note: The job is a remote job and is open to candidates in USA. CyberCoders is seeking a Data Scientist / Applied AI Engineer to turn data into production-ready machine learning solutions that drive business outcomes. The role involves managing the full machine learning lifecycle, including data extraction, feature engineering, model development, evaluation, deployment, monitoring, and collaboration with product and engineering teams.
Responsibilities
- Collaborate with stakeholders to define analytics questions, success metrics and deliverable timelines
- Extract, clean and transform data from relational databases and other sources using SQL, Python, pandas and polars
- Perform exploratory data analysis, feature engineering and statistical analysis to inform modeling choices
- Design, train and validate machine learning models using scikit-learn (sci-kit learn) and other appropriate libraries, ensuring robust model evaluation and selection
- Implement A/B tests and model evaluation procedures to measure business impact and iterate on models
- Productionize models and data pipelines: containerize, deploy and automate inference and monitoring workflows in collaboration with engineering teams
- Build reproducible, well-documented code, unit tests and versioned experiments; follow software engineering best practices and CI/CD for ML
- Create clear data visualizations and reports to communicate insights and model behavior to technical and non-technical stakeholders
- Monitor model performance in production, troubleshoot data and model drift, and implement retraining/maintenance strategies
Skills
- Bachelors or Masters degree in Computer Science, Statistics, Data Science, Engineering or related field, or equivalent practical experience
- Proven hands-on experience with Python and data libraries (pandas, polars) and strong SQL skills for data extraction and transformation
- Practical Data Science experience: data exploration, feature engineering, model training, and evaluation
- Experience with sci-kit learn (scikit-learn) for building and validating traditional ML models
- Solid understanding of statistical methods, metrics and experiment design (A/B testing)
- Ability to write clean, production-ready code, use version control (Git) and follow software engineering practices for reproducible pipelines
- Strong problem-solving skills, attention to detail, and the ability to communicate complex results clearly to stakeholders
- For this position, you must be currently authorized to work in the United States without the need for sponsorship for a non-immigrant visa
- Nice-to-have: deep learning experience, broader machine learning expertise, advanced data wrangling, feature engineering at scale, model deployment experience, model evaluation and monitoring, A/B testing implementation, and data visualization tools such as Seaborn and other plotting libraries
Benefits
- Paid Health Benefits (Medical Dental Vision)
- Equity with a 4 year vest
- Fully Remote Work Environment
Company Overview