Machine Learning Engineer

About METRA:
METRA Consulting Engineers was established in 1984 with the primary goal of planning and supervising infrastructure projects. The company has conducted comprehensive studies on roads, railways, and metro systems. Recently, METRA has expanded its capabilities to include advanced data analysis and artificial intelligence, enhancing its ability to deliver innovative and efficient solutions for infrastructure development.

About R&D Team:
At METRA, the R&D team is the driving force behind innovation and advancement. Our team is dedicated to pushing boundaries and exploring new possibilities to enhance our products and services. As a member of the R&D team, you will thrive in a dynamic and fast-paced environment where creativity and experimentation are highly valued. Our work spans a variety of challenges, from shaping critical business decisions to developing cutting-edge algorithms that power both our internal operations and customer-facing solutions. We collaborate closely with various departments to ensure our innovations lead to tangible improvements and meaningful impacts across the company.

Key Responsibilities:

1. Predictive Modeling and AI/ML Development:
   • Create and develop predictive models using AI/ML technologies.
   • Monitor and evaluate the performance of models in production.
   • Drive technical innovation through active research and application of new theories, techniques, and technologies.
2. Collaboration and Data Integration:
   • Partner with business and other teams to understand requirements and integrate additional data needed for model creation.
   • Work closely with engineers to deploy models in production in real-time and batch processes.
   • Systematically track and improve model performance.
3. Data Feature Engineering and Analysis:
   • Conduct data feature engineering to collect and structure data to enhance modeling and analytical frameworks.
   • Perform exploratory data analysis to identify trends and patterns, providing actionable recommendations.
4. Communication and Reporting:
   • Communicate non-technical reports detailing project outcomes and insights.
   • Advise on the application of data science insights in business strategies.
5. Quality Assurance and Best Practices:
   • Develop and implement data quality assurance strategies to ensure data integrity.
   • Stay updated with industry trends, tools, and best practices in data science.

Requirements:

• Minimum of 3 years of experience as a Data Scientist or in a similar role.
• Excellent understanding of machine learning techniques.
• Proficiency in Python libraries: Pandas, NumPy, SciPy, StatsModels, Scikit Learn, Seaborn, and Matplotlib.
• Knowledge and experience in deep learning and libraries such as TensorFlow, Keras, and PyTorch.
• Advanced SQL knowledge and experience with both relational and non-relational databases.
• Strong understanding of the model build lifecycle, including feature selection and optimization, model selection and validation, and ongoing model maintenance.

Preferred Qualifications:

• Knowledge and experience in image processing and NLP.
• Experience with big data technologies such as Hadoop, Spark, or Hive.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.