Machine Learning Engineer

About the role

The ideal candidate will assist in analyzing complex datasets, building predictive models, and collaborating with cross-functional teams to solve real-world business problems.

Responsibilities

• Analyze, clean, and preprocess structured and unstructured datasets.
• Develop and implement predictive models and machine learning algorithms.
• Perform exploratory data analysis to identify trends, patterns, and insights.
• Collaborate with engineering teams to deploy analytical models into production.
• Create dashboards, reports, and visualizations to communicate findings effectively.
• Validate data quality and ensure accuracy throughout the analytics process.
• Optimize data processing workflows and automate repetitive tasks.
• Work with cross-functional teams to understand business requirements and deliver data-driven solutions.
• Document methodologies, models, and technical processes.
• Stay updated with emerging technologies, machine learning techniques, and industry best practices.

Requirements

• Master's degree in Statistics, Mathematics, Computer Science, Engineering, Data Science, or a related quantitative field.
• 1–2 years of internship, academic project, or professional experience in data analytics or machine learning.
• Strong understanding of predictive modeling, machine learning algorithms, clustering, and classification techniques.
• Proficiency in at least one programming language such as Python, Java, C, C++, or SQL.
• Experience with data analysis libraries such as Pandas, NumPy, and Scikit-learn.
• Familiarity with Big Data technologies such as Hadoop, Spark, or Cassandra.
• Knowledge of data visualization tools such as Tableau, Power BI, or Matplotlib.
• Strong analytical, problem-solving, and communication skills.
• Ability to work collaboratively in a fast-paced team environment.

Preferred Skills

• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Knowledge of ETL processes and data pipelines.
• Understanding of REST APIs and data integration techniques.
• Experience with Git and version control systems.
• Exposure to Agile or Scrum development methodologies.