Note: The job is a remote job and is open to candidates in USA. TekWissen is a global workforce management provider whose client is a media and entertainment company. The Data Scientist will operate and improve production data and machine learning pipelines, manage workflow execution and dependencies, troubleshoot failures, and coordinate releases and recovery procedures. The role also focuses on data-quality validation, observability, documentation, automation, and stakeholder communication.
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
Skills
- • 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
Benefits
- Work Type: Remote
Company Overview
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