The AI/ML analyst is a person whose primary focus should be on researching, building, and designing self-running artificial intelligence (AI) systems to automate predictive models. He/she is responsible for designing and creating AI algorithms capable of learning and making predictions that define Machine Learning. He/she would be working closely with Data Architect, administrators, and data analysts.

What you will do:

• Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models
• Transforming data science prototypes and applying appropriate ML algorithms and tools
• Ensuring that algorithms generate accurate user recommendations.
• Turning unstructured data into useful information by auto-tagging images and text-to-speech conversions.
• Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.
• Developing ML algorithms to analyze huge volumes of historical data to make predictions.
• Running tests, performing statistical analysis, and interpreting test results.
• Documenting machine learning processes.
• Keeping abreast of developments in machine learning
• Researching and implementing ML algorithms and tools
• Selecting appropriate data sets
• Picking appropriate data representation methods
• Identifying differences in data distribution that affects model performance
• Verifying data quality.
• Transforming and converting data science prototypes.
• Performing statistical analysis.
• Running machine learning tests.
• Using results to improve models.
• Training and retraining systems when needed.
• Extending machine learning libraries.

What you will need to succeed:

• Bachelor s/Master s Degree in Computer Science, Mathematics and/or Statistics or an equivalent combination of education and experience.
• 3-5 Years of experience in AI/ML Analyst role.
• Proficiency with a deep learning framework such as TensorFlow or Keras
• A dvanced proficiency with Python, Java, and R code writing.
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture
• Advanced math and statistics skills, surrounding subjects such as linear algebra, calculus, and Bayesian statistics.
• Certification in machine learning, neural networks, deep learning, or related fields will be an added advantage.
• Good oral written communication skills.
• Strong analytical, problem-solving and teamwork skills.
• Software engineering skills.
• Experience in Data Science.
• Coding and programming languages, including Python, Java, C , C, R and JavaScript.
• Experience in working with ML frameworks.
• Understand data structures, data modeling and software architecture.
• Knowledge in computer architecture.