Job Description : 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 : - 6 years of IT experience in which at least 3 years of relevant experience primarily in converting data science prototypes and deploying models to production - Proficiency with Python and machine learning libraries such as pandas, xgboost - Strong working experience with pyspark - Experience with Machine Learning life cycle and training/retraining - Strong expertise in using kubeflow/airflow and docker containerization - Knowledge of Big Data frameworks like Hadoop, Spark, etc - Experience in working with ML frameworks like Tensor Flow, Keras, Open CV - 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 Scala, 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 (ref:hirist.tech)