Data Scientist Engineer

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.
• Exploring and visualizing data to gain an understanding of it,
• Identifying differences in data distribution that could affect performance when deploying the model in the real world
• Verifying data quality, and/or ensuring it via data cleaning
• Supervising the data acquisition process if more data is needed
• Defining the preprocessing or feature engineering to be done on a given dataset
• Defining validation strategies
• Training models and tuning their hyperparameters
• Analyzing the errors of the model and designing strategies to overcome them
• Deploying models to production

Qualifications:

• Bachelor’s degree or equivalent experience in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.)
• At least 1 – 2 years of experience in quantitative analytics or data modeling
• Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms
• Fluency in a programming language (Python, C, C++, Java, SQL)
• Proficiency with a Deep Learning Framework such as TensorFlow or Keras
• Proficiency with Python and basic libraries for machine learning such as sci-kit-learn and pandas
• Proficiency with data visualization libraries such as seaborn, matplotlib, etc.
• Proficiency with Probability and Statistic
• Proficiency with OpenCV
• Familiarity with at least one of Python web frameworks (Flask, Django, etc.)
• Familiarity with Linux
• Familiarity with Cloud Platforms such as Google Cloud Platform and Alicloud
• Familiarity with CI/CD
• Ability to collaborate and communicate with various stakeholder
• Able to demonstrate Critical Thinking