- Advanced degree in computer science, math, statistics, or a related discipline is preferable but knowledge of the domain is more important than a piece of paper.
- Should have good knowledge of math and statistics.
- 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.
- 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.
- Verifying data quality, and/or ensuring it via data cleaning.
- Supervising the data acquisition process if more data is needed.
- Finding available datasets online that could be used for training. Defining the preprocessing or feature engineering to be done on a given dataset.
- Defining data augmentation pipelines.
- Training models and tuning their hyperparameters.
- Analyzing the errors of the model and designing strategies to overcome them.
- Deploying models to production.
- Proficiency with a deep learning framework such as TensorFlow or Keras or PyTorch.
- Proficiency with Python and basic libraries for machine learning such as sci-kit-learn, pandas, and NumPy.
- Expertise in visualizing and manipulating big datasets.
- Proficiency with OpenCV.
- Familiarity with Linux.
- Ability to select hardware to run an ML model with the required latency.
- Competitive Salary
- Start-up environment which allows for creativity and fun
- A pleasant working environment with enthusiastic and friendly teams
- Possibility of personal growth and to take over responsibilities
- Flexible work hours
- Employee referral bonus program