Data / ML Engineer Offline

- Multi-tenancy & medium-high data volume processing

- Data collected from smart devices is accessed from cloud (AWS) storage and undergoes translation from a device-specific schema, file formats, etc and transformations such as a selection of relevant data and features before being applied to a ML model training subsystem; qualified models are then pushed to the production environment for prediction/execution.

- Data handling employs scalable Spark-based access. The entire processing workflow is kept in sync via pipelines defined in Airflow.

- The state of the entire data engineering (& ML models, training and execution) is available via Dashboard UI.

Responsibilities:

• Bring data engineering/machine learning expertise to a local Scrum team, providing qualified deliverables and services on schedule.

• Participate in progress reviews

• Work with scrum master(s), tech lead(s) to analyze and understand user stories in each sprint

• Complete coding & unit testing for the allotted stories

• Create design documents or make changes to existing ones

• Complete code reviews

Mandatory Skills:

- At least 2 years of experience working on data science/machine learning problems

- Knowledge of a variety of machine learning techniques (clustering, random forests, neural networks, etc.) and their real-world advantages/drawbacks.

- Experience working in Python

- Experience working in Data frameworks such as Spark, Hadoop, HDFS

- Experience working with ML frameworks (Pytorch, Tensorflow, etc.) and libraries (scikit-learn, pandas)

- Experience with OOP software engineering practices

- Knowledge of Apache Airflow

Nice-to-Have Skills:

-Some experience with AWS

-Experience working in IoT or predictive maintenance domain

-Experience visualizing/presenting data for stakeholders using: D3, Qlik sense, ggplot