Data Engineer

• Proficiency with Python or R, SQL and familiarity with working in big data environments (2 years minimum)

• At least 3+ years of experience of deploying machine learning models and frameworks in production

• At least 5 years of experience with:

o Experiences in both Supervised and Unsupervised machine learning models (Regressions, Gradient Boosted methods, SVMs, Random Forests, Clustering)

o Familiar with python packages such as Pandas, SkLearn, Numpy etc

o Time Series Analysis (ARIMA, SARIMAX, MA)

o Deep learning (CNN, RNN, LSTM) and framework library (e.g., Keras, TensorFlow, PyTorch) and Graph Neural Networks

o End to end machine learning lifecycle experience including feature engineering, model building, model versioning, evaluation and hypothesis testing.

• At least 3 years of experience with

o Cloud computing platforms such as AWS, Azure (preferably AWS)

o Containerization and orchestration technologies such as Docker and Kubernetes.

o Standard concepts and technologies used in CI/CD build and deployment pipelines.

• At least 2 years of experience in distributed computing frameworks such as Spark or Pyspark is desirable.