Job Description

The role reports to the SVP of Content Data Science. This represents a uniquely exciting opportunity for a machine learning engineer to deliver impact at a leading premium global content company, in an exciting and rapidly-evolving industry.

We are seeking individuals with deep technical skills who seek to be the smartest in the room and are not afraid of being surrounded by smarter ones, individuals who are results-oriented enough to work on multi-year projects that have the potential to build significant value across all parts of our business.

About you. You must be:

Feel very comfortable asking a lot of questions

Comfortable with Kanban and Scrum as well as no structure at all because you understand that one model does not fit all

Apt at being at time a leader and at other time a follower, possibly with the same people regardless of ranks and seniority, depending on how you contribute to a project

Enjoy written communication because it gives you a medium to express highly complex and nuanced ideas

Overview & responsibilities:

The Machine Learning Engineer should possess a deep sense of curiosity and a passion for building decision supporting products based on data, and the ability to communicate data structures and tools throughout the ViacomCBS Content Optimization team. This engineer supports data pipeline development which includes machine learning algorithms using disparate data sources, from large 100+Tb to small Excel spreadsheets stored on AWS or GCP.

Preference is to be based in Atlanta, GA but may work remotely within the US

Works with large, medium and tiny volumes of data in diverse formats and stores to build pipelines and data migration tools that enhance raw data.

Able to break down and communicate highly complex data problems into simple, feasible solutions.

Extract patterns from large datasets and transform data into an informational advantage.

Partnering with multiple teams across ViacomCBS to determine the best approach around data ingestion, structure, and storage. Then, working with the team to ensure these are implemented correctly.

Contributing ideas on how to make our data more effective and working with other members of the team and other teams to implement changes.

Ongoing development of technical solutions while developing and maintaining documentation, at times training impacted teams.

Early on collaboration with the team on internal initiatives to create strategies that improve company processes.

Required qualifications:

4+ years work experience with focus on implementing data pipelines for data products.

Experience building and deploying applications on AWS

Familiarity with distributed computed patterns and frameworks (Spark, AWS EMR, AWS Batch, Dask, Ray, Modin)

Orchestration and data cataloging tools (AWS Glue, Airflow)

Experience with MLOps (MLFlow, Kubeflow, Seldon) and DevOps (Jenkins, Git, monitoring tools)

Familiarity with distributed query engines such as Redshift, Athena, BigQuery, Snowflake, including experience with query performance tuning, file formats such as Parquet, Avro

Experience with large data volumes (100+ tb)

Advanced Python, including familiarity with OOP and large codes, as well as data science libraries (Pandas, NumPy)

Desired additional qualifications:

Master degree in CS or similar fields, or unusual career path

Experience as DBA

Experience as full stack developer