Overview

We are seeking an experienced Senior Data Scientist with a strong background in non–deep learning machine learning approaches and the ability to apply deep learning to raw, non-textual data. This role will focus on creating custom ML models tailored to the needs of customers, ensuring that the solutions are accurate, reliable, and production-ready.

You will be responsible for the end-to-end lifecycle of customer-focused data science projects—ranging from data analysis, model development, and evaluation to integration into production systems—while maintaining high standards of quality and performance.

Key Responsibilities

• Design, develop, and deploy machine learning models with a focus on non–deep learning approaches (e.g., tree-based methods, ensemble models, probabilistic models, clustering, anomaly detection).
• Apply deep learning techniques to raw, non-textual data where applicable.
• Conduct in-depth data analysis, including outlier detection and creation of data quality metrics to ensure robust model performance.
• Develop solutions for anomaly detection, data visualization/reporting, clustering, and classification tasks.
• Perform feature engineering, data preprocessing, and exploratory analysis for large and complex datasets.
• Collaborate with engineering teams to integrate models into production environments.
• Write production-grade, object-oriented Python code, ensuring maintainability and scalability.
• Communicate results and insights effectively to both technical and non-technical stakeholders.
  
  

Qualifications

Required:

• 5+ years of industrial experience in machine learning, with substantial expertise in non–deep learning approaches.
• Proven track record of applying deep learning to raw, non-textual datasets.
• Strong background in data analysis, outlier detection, data quality metrics, anomaly detection, clustering, and classification.
• Advanced proficiency in Python, including writing production-grade, object-oriented code (beyond Jupyter notebooks).
• Solid understanding of statistics, model evaluation, and validation techniques.
• Strong critical thinking and problem-solving skills.
• Excellent communication skills and ability to work in a dynamic, fast-paced environment.
  
  

Nice To Have

• Experience working with customer-specific ML solutions.
• Familiarity with the Azure cloud platform.
• Experience in startup or small-team environments.