Machine Learning Engineer

Job Description

Batch- 2023/2022 & earlier (Master’s Degree or Higher)

Responsibilities:

• Build machine learning systems.
• Offering Data Science Excellence to team in DS related modules.
• Deeper understanding of DS methodology, approaches, tools and been able to formulate a business problem into a solution.
• Transform data science prototypes into machine learning systems.
• Build cost effective and efficient systems using parallel processing and other methods.
• Run machine learning tests and experiments.
• Perform Statistical Analysis and Fine-Tuning based on Test results.
• Research and implement appropriate ML algorithms and tools.
• Select appropriate datasets and data representation methods.
• Train and retrain systems when necessary.
• Collaboration with Data Engineering, BI, and other teams.

Experience Required:

• Proven experience as ML engineer or similar role.
• Experience in developing ML systems or ML products.
• Understanding of data structures, data modelling and software architecture.
• Deep knowledge of math, probability, statistics, and algorithms.
• Ability to write robust code in Python and R.
• Experienced in Big Data technologies like Spark and Hadoop.
• Effective communication skills.
• Involvement in business problem identification, proposing solutions and evaluating them, identifying required data sources, building data pipeline, visualizing the outputs, and taking actions based on the data outputs.
• Experience in solving data science problems using machine learning, data mining algorithms and big data tools.
• Strong Experience with at-least one programming language – e.g., Java, Python, R (Exposure to multiple is a plus)
• Storing Experience in delivering data science projects leveraging cloud infrastructure.
• Highly passionate about making an impact on business using data science.
• Believes in continuous learning and sharing knowledge with peers.

Education:

• Master’s degree or higher in one or more of the following quantitative disciplines, i.e., Mathematics, Statistics, Economics, Computing.