Job Responsibilities:

• 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.

Job Requirements:

• Bachelor's degree in computer science, data science, mathematics, or a related field.
• Knowledge as a machine learning engineer. Proficiency with a deep learning framework such as TensorFlow, XgBoost, Wavevnet, Keras, numpy.
• Advanced proficiency with Python, Java, and R code writing.
• Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas.
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture in ANN, CNN, RNN with LSTM.
• 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.