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.
- Experience in working with ML frameworks.
- Understand data structures, data modeling and software architecture.
- Knowledge in computer architecture.
supplier.io provides solutions that help supplier diversity managers implement highly effective strategies to manage and grow their programs. Over 150 companies use our data and services to identify, qualify, onboard, and track their diverse suppliers.