Job Duties and Responsibilities

Providing solutions for the deployment, execution, validation, monitoring, and improvement of

data science solutions.

Creating high performance and scalable Machine Learning systems

Building reusable production data pipelines for machine learning models

Collaborating with Data Engineers and Data Scientist to build data and model pipelines and help

in running machine learning tests and experiments

Helping to manage the infrastructure and data pipelines needed to bring an Client solution to

production

Troubleshooting production machine learning model issues, including recommendations for

retrain, revalidate, and improvements

We are seeking people who are passionate about:

Working with data in all forms (structured, unstructured, images, etc) and using it creatively to

help solve problems

Collaborating with data scientists and data engineers to make a big impact

Staying in-tune with the big data community and encouraging the organization to utilize cutting

edge technologies to innovate and invent solutions.

Skills - Experience and Requirements

You would be considered a great fit for this role if you have the following:

Bachelor's degree in computer science engineering, Data Science, or a related technical degree.

In-depth knowledge of machine learning data structures and modelling, software architecture,

libraries and frameworks

Software development experience using Java and related Client libraries including strong

understanding of software engineering principles.

Effective oral and written communication skills to collaborate with others.

Sound problem-solving skills to refine prototypes and troubleshoot performance issues.

These qualifications would make you stand out among other applicants:

Experience deploying and managing containerized applications, preferably using AWS

Kubernetes Services

Experience with big data tools and processing technologies such as Apache Spark, Apache Flink,

and cloud platforms like AWS

Experience in Java or Python.

Familiarity with data-oriented workflow orchestration frameworks such as Kubeflow, Airflow