Senior Machine Learning Engineer
STAFIDE
Role description
As a Senior Machine Learning Engineer, you will:
- Design, develop, and deploy machine learning solutions to solve complex business problems.
- Build scalable data processing and analytics solutions using Python and SQL.
- Develop statistical and machine learning models to support predictive analytics and data-driven decision-making.
- Analyze, clean, and transform structured and unstructured datasets for model development.
- Work with cloud-based analytics and machine learning platforms, including Azure Machine Learning Studio, Azure Databricks, AWS, and Google Cloud Platform (GCP).
- Collaborate with cross-functional teams to understand business requirements and translate them into scalable AI and data science solutions.
- Optimize model performance through feature engineering, model evaluation, and continuous improvement.
- Develop reusable data science workflows and maintain high standards for code quality and documentation.
- Support deployment, monitoring, and maintenance of machine learning solutions in cloud environments.
- Stay updated with emerging technologies and best practices in machine learning, cloud computing, and data engineering.
What You Bring to the Table:
- 8–10 years of professional experience in Python development, Data Science, Machine Learning, or Analytics Engineering.
- Strong programming expertise in Python with hands-on experience using Scikit-learn, Pandas, NumPy, Matplotlib, statsmodels, and related data science libraries.
- Working knowledge of R for statistical computing and data analysis.
- Strong SQL skills with experience working on enterprise data platforms such as Teradata and BigQuery.
- Solid understanding of machine learning algorithms, model training, validation, and evaluation techniques.
- Experience in statistical analysis, predictive modeling, and data exploration.
- Familiarity with cloud-based analytics and machine learning platforms, including Azure Machine Learning Studio, Azure Databricks, Google Cloud Platform (GCP), and Amazon Web Services (AWS).
- Experience working with large datasets and designing scalable analytics solutions.
- Strong analytical, problem-solving, and communication skills.
- Ability to quickly learn new technologies and adapt to evolving business requirements.
You Should Possess the Ability to:
- Design and implement end-to-end machine learning solutions.
- Develop scalable data processing pipelines and analytical models.
- Apply statistical techniques to extract meaningful business insights.
- Build, evaluate, and optimize machine learning models using industry best practices.
- Work with cloud-native machine learning and analytics platforms.
- Analyze large and complex datasets using SQL and Python.
- Collaborate effectively with data engineers, analysts, architects, and business stakeholders.
- Troubleshoot technical issues and optimize model performance.
- Communicate technical concepts clearly to both technical and non-technical audiences.
- Deliver high-quality solutions while maintaining a strong focus on accuracy, scalability, and continuous improvement.
What We Bring to the Table:
- Opportunity to work on enterprise-scale data science and machine learning initiatives.
- Exposure to modern cloud platforms, advanced analytics, and AI technologies.
- A collaborative environment focused on innovation, continuous learning, and technical excellence.
- Opportunities to work with experienced engineers, data scientists, and cloud architects.
- Challenging projects involving Python, cloud-native machine learning, statistical modeling, and enterprise analytics.
- A culture that encourages ownership, knowledge sharing, and professional growth.
- Continuous opportunities to enhance expertise in cloud comp uting, machine learning, and advanced data engineering.
