Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress.
Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability. Determine and refine machine learning objectives.
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.
Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
Ensuring that algorithms generate accurate user recommendations.
Verifying data quality, and/or ensuring it via data cleaning.
Supervising the data acquisition process if more data is needed.
Defining validation strategies.
Defining the pre-processing or feature engineering to be done on a given dataset
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.
Deploying models to production.
Documenting machine learning processes.
Keeping abreast of developments in machine learning.
Machine Learning Engineer Requirements:
Bachelor's degree in computer science, data science, mathematics, or a related field.
At least two years of experience as a machine learning engineer. Proficiency with a deep learning framework such as TensorFlow, XgBoost, Devnet, Keras, NumPy.
Advanced proficiency with Python, Java, and R code writing.
Proficiency with Python and basic libraries for machine learning such as sci-kit-learn and pandas
Extensive knowledge of ML frameworks, libraries, data structures, data modelling, and software architecture.
Ability to select hardware to run an ML model with the required latency
In-depth knowledge of mathematics, statistics, and algorithms.
Superb analytical and problem-solving abilities.
Great communication and collaboration skills.
Excellent time management and organizational abilities.