Job Description
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
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.
Numbers & Facts
| Location | New York, NY (Remote) |
| Industry | Computer/IT Services |
| Salary | $62.12–$100.95 |
| Company Size | 100 to 499 employees |
| Year Founded | 2009 |
| Website | http://www.tekwissen.com/ |
About Company
WE THE TEKWISSEN PEOPLE
TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.
To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.
Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.
It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.
Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.Skills
- Amazon Web Services (AWS)unmatched
- Amusement Parksunmatched
- Automationunmatched
- Cloud Computingunmatched
- Computer Scienceunmatched
- Data Analysisunmatched
- Data Managementunmatched
- Data Modelingunmatched
- Data Qualityunmatched
- Data Recoveryunmatched
- Data Scienceunmatched
- Data Setsunmatched
- Diversityunmatched
- Documentationunmatched
- Economicsunmatched
- Entertainment and Mediaunmatched
- Filmunmatched
- Identify Issuesunmatched
- Machine Learningunmatched
- Mathematicsunmatched
- Microsoft Windows Azureunmatched
- Model Validationunmatched
- Operations Researchunmatched
- Presentation/Verbal Skillsunmatched
- Production Supportunmatched
- Python Programming/Scripting Languageunmatched
- Quality Assurance Methodologyunmatched
- Quantitative Researchunmatched
- Reporting Dashboardsunmatched
- Riskunmatched
- SQL (Structured Query Language)unmatched
- Schedule Developmentunmatched
- Source Code/Configuration Management (SCM)unmatched
- Statisticsunmatched
- Streaming Technologyunmatched
- Team Playerunmatched
- Television Broadcastingunmatched
- Television Newsunmatched
- Test Automationunmatched
- Workflow Analysisunmatched
- Workforce Managementunmatched
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