Data Scientist III

Eliassen Group; Charlotte (NC); Hybrid; Confidential; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401k with company matching

Life insurance

Job summary

Eliassen Group has a client in Charlotte, NC seeking a Data Scientist III focused on time series forecasting to support advertising viewership and inventory management. The role is hybrid (1/4 on-site) and works in an AWS environment with SageMaker, Python, SQL, Snowflake, Spark, Airflow, and AWS Glue.

You will investigate first-party data, integrate it into forecasting models, collaborate with data engineers, and clearly communicate methods and results to stakeholders.

Qualifications

• 5 to 10 years of experience in data science with strong time series forecasting background.
• Proficiency in Python and SQL.
• Experience working in cloud-based environments.
• Strong mathematical and statistical foundation with ability to explain methods and findings.
• Experience with large-scale datasets, Spark, and orchestration tools such as Airflow.
• Nice to have: data engineering experience and optimization exposure, including mixed-integer optimization using tools such as CPLEX or Gurobi.

Responsibilities

• Investigate and assess new first-party data sources for relevance to forecasting objectives.
• Integrate new data into existing time series forecasting models or develop new models when required.
• Forecast linear and digital viewership to inform inventory planning and booking decisions.
• Map projected audiences to available advertising inventory to support campaign commitments and SLAs.
• Collaborate with data engineers to operationalize models using pipelines, workflows, and production processes.
• Develop, document, and communicate modeling approaches, assumptions, and results to technical and non-technical stakeholders.
• Work within an AWS-centric environment leveraging Python, SQL, Snowflake, SageMaker, Spark, Airflow, and AWS Glue.
• Partner with upstream data provider teams to consume cleansed and prepared data.

Skills

Python

SQL

Time series forecasting

Cloud-based environments

Spark

Airflow

Tools

Snowflake

SageMaker

Airflow

AWS Glue

Job description

Description:

Hybrid 1/4 in Charlotte, NC

Our client seeks a Data Scientist III focused on time series forecasting to support advertising viewership and inventory management. The role will investigate incoming first-party data and integrate it into existing forecasting models or develop new models as needed. The team operates in AWS with production workloads in SageMaker and related services. Collaboration with data engineers and reporting teams is expected, with clear communication of methods and results.

Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $70.00 to $80.00/hr. w2

Responsibilities:

• Investigate and assess new first-party data sources for relevance to forecasting objectives.
• Integrate new data into existing time series forecasting models or develop new models when required.
• Forecast linear and digital viewership to inform inventory planning and booking decisions.
• Map projected audiences to available advertising inventory to support campaign commitments and SLAs.
• Collaborate with data engineers to operationalize models using pipelines, workflows, and production processes.
• Develop, document, and communicate modeling approaches, assumptions, and results to technical and non-technical stakeholders.
• Work within an AWS-centric environment leveraging Python, SQL, Snowflake, SageMaker, Spark, Airflow, and AWS Glue.
• Partner with upstream data provider teams to consume cleansed and prepared data.

Experience Requirements:

• 5 to 10 years of experience in data science with strong time series forecasting background.
• Proficiency in Python and SQL.
• Experience working in cloud-based environments
• Strong mathematical and statistical foundation with ability to explain methods and findings.
• Experience with large-scale datasets, Spark, and orchestration tools such as Airflow.
• Nice to have: data engineering experience and optimization exposure, including mixed-integer optimization using tools such as CPLEX or Gurobi.