Data Scientist

Overview:

TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client is a media and entertainment company that develops, produces, and markets entertainment, news, and information. It owns and operates a portfolio of news and entertainment television networks, a motion picture company, television production operations, a television stations group, theme parks, and a suite of Internet-based businesses.

Position: Data Scientist
Location: New York, NY 10112
Duration: 12 Months
Job Type: Temporary Assignment
Work Type: Remote
Shift: Monday To Friday From 9:00 AM To 6:00 PM

Job Description:

Responsibilities:

• Own the day-to-day health and execution of production model pipelines and the data pipelines that supply them.
• Manage recurring refresh schedules, dependencies, handoffs, and delivery commitments across batch and near-real-time workflows.
• Monitor pipeline runs, data freshness, model outputs, and downstream availability; identify emerging issues before they affect stakeholders.
• Lead triage and resolution when workflows fail or outputs are delayed, coordinating the right data science, platform, data, and business partners through closure.
• Diagnose root causes across source data, transformations, runtime environments, orchestration, model execution, and downstream delivery.
• Create automated checks for data quality, schema changes, completeness, reasonableness, and model-output validation.
• Partner with data scientists to translate notebooks and analytical workflows into repeatable, supportable production processes.
• Coordinate releases, model updates, backfills, reruns, and recovery procedures with clear validation and stakeholder communication.
• Improve observability through practical logging, alerting, run histories, service expectations, and operational dashboards.
• Maintain current documentation for pipeline logic, dependencies, ownership, schedules, recovery steps, and known constraints.
• Reduce recurring manual work and operational risk through reusable patterns, automation, and thoughtful workflow simplification.
• Communicate pipeline status, incidents, risks, and resolution plans clearly to both technical and nontechnical stakeholders.

Qualifications:

• Advanced (Master or PhD) degree with specialization in Statistics, Computer Science, Data Science, Economics, Mathematics, Operations Research or another quantitative field or equivalent.
• 5+ years of hands-on experience supporting production data, analytics, or machine learning workflows in a complex environment.
• Advanced proficiency in Python and SQL, including the ability to read, troubleshoot, and improve unfamiliar production code.
• Demonstrated experience building, operating, and troubleshooting scheduled data and model pipelines at scale.
• Strong understanding of data dependencies, schema evolution, backfills, idempotent processing, failure recovery, and data-quality controls.
• Ability to diagnose issues across multiple systems and drive resolution when ownership is distributed across teams.
• Clear written and verbal communication, including concise incident updates, operating documentation, and stakeholder-ready explanations.
• Experience with workflow orchestration, version control, automated testing, release practices, and cloud-based data platforms.
• Experience supporting machine learning lifecycle workflows, including training, scoring, validation, deployment coordination, and monitoring.
• Experience with tools such as Airflow, Databricks, Spark, dbt, Kubernetes, or comparable workflow and data platforms.
• Experience with cloud services such as Google Cloud, AWS, or Azure and with large relational or non-relational datasets.
• Familiarity with streaming or event-driven data workflows using technologies such as Kafka, Spark Streaming, or Flink.
• A proactive operating style that spots risks early, follows issues through closure, and improves the system after recovery.
• Strong ownership, sound judgment, and comfort balancing immediate restoration with durable prevention.
• A collaborative approach that builds trust with data scientists, platform specialists, data owners, and business partners.
• Interest in media, streaming, customer analytics, or large-scale digital products.

TekWissen Group is an equal opportunity employer supporting workforce diversity.