Senior Manager, Data Science

Jobtailor; Marysville (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor is seeking a Lead Data Science professional to head a team delivering production-grade ML and analytics across forecasting, causal modeling, and classification. You will set robust evaluation standards, and guide data-driven decisions with measurable business impact.

Responsibilities include integrating AI coding and analysis agents into daily workflows, owning ML CI/CD, model/data versioning, lineage, and monitoring.

Qualifications

• Strong ML foundations with solid grounding in ML algorithms and statistics.
• Production-quality Python; reproducible pipelines; fluent with Git, testing, containers, and APIs.
• Hands-on experience operating a mature ML lifecycle: CI/CD for ML, model and data versioning and lineage, monitoring, retraining, rollback, and governance at scale.
• Confident daily use of AI coding and analysis tools; understanding of LLM evaluation, RAG, embeddings, vector search, and agent workflows.
• Track record leading and growing a data science team, coaching individuals, and prioritizing across competing stakeholders.
• Demonstrated ability to tie modeling work to measurable business outcomes and to explain it to non-technical leaders.

Responsibilities

• Lead a data science team turning commercial questions into production ML and analytics.
• Deliver models across families: time-series forecasting, causal modeling, regression, classification.
• Set evaluation standards for models and define success metrics.
• Integrate AI coding and analysis agents into daily workflow to automate work.
• Own CI/CD for ML, model and data versioning, lineage tracking, and monitoring.
• Collaborate with stakeholders to translate business needs into technical roadmaps.

Skills

ML Foundations

Production-Quality Python

Git & CI/CD

Experiment Tracking

Containerization

APIs

Model Versioning

Lineage & Monitoring

LLM Evaluation

Embeddings & Vector Search

Agent Workflows

Leadership & Coaching

Stakeholder Communication

Business Impact Focus

Tools

Git

Testing

Containers

APIs

Model Versioning

Lineage Tracking

Retraining

Rollback

Governance

AI Coding Tools

Job description

• Lead a data science team turning commercial questions into production ML and analytics


• Deliver models across families: time-series forecasting, causal modeling, regression, classification


• Set evaluation standards for models and define success metrics


• Integrate AI coding and analysis agents into daily workflow to automate work


• Own CI/CD for ML, model and data versioning, lineage tracking, and monitoring

Requirements

• Strong ML foundations with solid grounding in ML algorithms and statistics


• Production-quality Python; reproducible pipelines; fluent with Git, testing, containers, and APIs


• Hands-on experience operating a mature ML lifecycle: CI/CD for ML, model and data versioning and lineage, monitoring, retraining, rollback, and governance at scale


• Confident daily use of AI coding and analysis tools; understanding of LLM evaluation, RAG, embeddings, vector search, and agent workflows


• Track record leading and growing a data science team, coaching individuals, and prioritizing across competing stakeholders


• Demonstrated ability to tie modeling work to measurable business outcomes and to explain it to non-technical leaders.

Core Competencies

Demonstrates expertise in leading data science teams to deliver production-quality machine learning models and analytics, with a strong focus on CI/CD processes, model evaluation, and business impact. Proficient in integrating AI tools into workflows and communicating complex concepts to non-technical stakeholders.

Highest-signal resume keywords

• Machine Learning Foundations


• Production-Quality Python


• CI/CD for ML


• AI Coding and Analysis Tools


• Data Science Team Leadership

ATS Optimization Keywords

Hard Skills

• Machine Learning Algorithms


• Statistics


• Time-Series Forecasting


• Causal Modeling


• Regression


• Classification


• Model Evaluation Standards


• Success Metrics


• Reproducible Pipelines


• Monitoring

Soft Skills

• Coaching


• Prioritization


• Communication

Industry Keywords

• ML Lifecycle


• Business Outcomes


• Stakeholder Management


• LLM Evaluation


• RAG


• Embeddings


• Vector Search


• Agent Workflows

Tools & Technologies

• Git


• Testing


• Containers


• APIs


• Model Versioning


• Lineage Tracking


• Retraining


• Rollback


• Governance


• AI Coding Tools