Machine Learning (ML) Engineer

Responsibilities:

• Studying, transforming, and converting data science prototypes
• Deploying models to production
• Training and retraining models as needed
• Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their respective scores
• Analyzing the errors of the model and designing strategies to overcome them
• Identifying differences in data distribution that could affect model performance in real-world situations
• Performing statistical analysis and using results to improve models
• Supervising the data acquisition process if more data is needed
• Defining data augmentation pipelines
• Defining the pre-processing or feature engineering to be done on a given dataset
• To extend and enrich existing ML frameworks and libraries
• Understanding when the findings can be applied to business decisions
• Documenting machine learning processes

Basic requirements: 

• 4+ years of IT experience in which at least 2+ years of relevant experience primarily in converting data science prototypes and deploying models to production
• Proficiency with Python and machine learning libraries such as scikit-learn, matplotlib, seaborn and pandas
• Knowledge of Big Data frameworks like Hadoop, Spark, Pig, Hive, Flume, etc
• Experience in working with ML frameworks like TensorFlow, Keras, OpenCV
• Strong written and verbal communications
• Excellent interpersonal and collaboration skills.
• Expertise in visualizing and manipulating big datasets
• Familiarity with Linux
• Ability to select hardware to run an ML model with the required latency
• Robust data modelling and data architecture skills.
• Advanced degree in Computer Science/Math/Statistics or a related discipline.
• Advanced Math and Statistics skills (linear algebra, calculus, Bayesian statistics, mean, median, variance, etc.)

Nice to have

• Familiarity with Java, and R code writing.
• 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