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Location: Remote EST Hours
Duration: Multi Year Contract
Target Rate: 70-95/hr
We are seeking a highly skilled Lead Data Science & AI Architect with a strong background in data mining, predictive modeling, and deep learning. This role focuses on building high‑performing analytical models. The ideal candidate is a hands‑on technical leader experienced in Python, classical and deep learning methods, and end‑to‑end model architecture. You will guide technical strategy, mentor team members, collaborate with stakeholders, and drive the development of advanced data‑driven solutions.
- Architect, design, and implement advanced machine learning and deep learning models.
- Build predictive models, including classification, regression, anomaly detection, and time‑series forecasting.
- Develop and refine data mining workflows to extract meaningful signals from large, complex datasets.
- Write high‑quality, production‑ready Python code using frameworks such as PyTorch, TensorFlow, scikit‑learn, and related libraries.
- Serve as the technical lead for data science initiatives, establishing best practices and guiding modeling strategy.
- Ensure model performance, reliability, and scalability through rigorous evaluation and iterative improvement.
- Lead exploratory data analysis (EDA), feature engineering, and dataset preparation for modeling.
- Identify patterns, correlations, and opportunities for predictive modeling across diverse datasets.
- Validate data quality, assumptions, and statistical integrity throughout the development lifecycle.
Required Qualifications
- 6+ years of experience in Data Science.
- Experience leading technical projects or serving in a senior/lead capacity.
- Demonstrated expertise in data mining, predictive analytics, and building production‑grade ML/DL models.
- Strong proficiency in Python, including data science and deep learning libraries.
- Experience designing and implementing models such as:
- Deep neural networks (CNN, RNN, LSTM, etc.)
- Classic ML models (logistic regression, random forest, gradient boosting, SVM, etc.)
- Classification and regression solutions
- Solid understanding of statistics, model evaluation metrics, and algorithm selection.
- Ability to define solution architecture, drive technical direction, and mentor other data scientists.
