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1 month ago

Job Summary

Join our dynamic team as a Data Analyst & Machine Learning Engineer, where you will leverage your expertise to extract valuable insights from data and develop machine learning models. As a key member of Eryk IT, you will play a crucial role in transforming data into actionable intelligence and contributing to innovative solutions for our clients.

  • Minimum Qualification: Degree
  • Experience Level: Mid level
  • Experience Length: 5 years

Job Description/Requirements


• Perform exploratory data analysis, clean and preprocess raw data, and derive meaningful insights to inform business decisions. 

• Design, implement, and evaluate machine learning models to solve complex business problems and enhance decision-making processes.

• Identify and create relevant features from structured and unstructured data sources to improve model performance.

• Communicate findings and insights effectively through the creation of visualizations, dashboards, and reports for both technical and non-technical stakeholders.

• Work closely with cross-functional teams, including domain experts, data engineers, and software developers, to integrate data-driven solutions into our products and services.

• Collaborate with the engineering team to deploy machine learning models into production environments and monitor their performance.


• Master’s or Ph.D. in Computer Science, Statistics, Data Science, or a related field..

• Minimum of 3 years of hands-on experience in data analysis and machine learning model development.

• Proficient in programming languages such as Python or R, with a strong understanding of relevant libraries and frameworks (e.g., Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch).

• Solid foundation in statistical methods and hypothesis testing for rigorous data analysis.

• Expertise in manipulating and analyzing large datasets, including knowledge of SQL for data extraction and manipulation.

• In-depth knowledge of a variety of machine learning algorithms and their applications, including supervised and unsupervised learning techniques.

• English in writing and speaking.

• Familiarity with big data technologies and frameworks (e.g., Apache Spark, Hadoop) for handling and processing large-scale datasets.

• Experience with deep learning architectures and frameworks (e.g., TensorFlow, PyTorch)

• Understanding of data engineering principles, ETL processes, and data pipeline

• Experience working with cloud platforms such as AWS, Azure, or Google Cloud for scalable data storage and processing.

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