Top 2 Part time library technical assistant Jobs in Columbus

Sr. Manager Data Science
The Scotts Miracle-Gro Company —Marysville, OH
- $175,700 - $206,700 a year
- Full-time
- Management
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- Supervising experience
- Health insurance
- Dental insurance
Physician Clinical Faculty, Radiation Oncologist-GU (Open Rank/Track Faculty)
The Ohio State University Wexner Medical Center —Columbus, OH
- Online Application
- In person
- Background check
- Medicine
- BC/BE
- Health insurance
- Dental insurance

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Sr. Manager Data Science
The Scotts Miracle-Gro Company —Marysville, OH
Lead a data science team that turns Scotts' commercial questions into production ML and analytics, pricing and elasticities, demand and POS forecasting,…
$175,700 - $206,700 a year
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Cedarville University —Cedarville, OH
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Sr. Manager Data Science

The Scotts Miracle-Gro Company -
Marysville, OH

Apply Now

Job Details

Full-time
$175,700 - $206,700 a year

Benefits

Health insurance
Dental insurance
Vision insurance
401(k) matching

Qualifications

AI models
Performance monitoring
Gradient boosting machines
Management
Predictive modeling analysis
Team supervision
Software coding
Coaching
Talent management
AI platforms (beyond public GPTs)
Managing data teams
Maintaining data pipelines
Team development
Production systems
Supervising experience
Model deployment
Version control systems
Continuous integration
Leading team collaboration initiatives
APIs
Software testing
Predictive analytics projects
Model evaluation
Machine learning frameworks
Python
MLOps

Full Job Description

Here at Scotts Miracle-Gro there is no such thing as a typical day. Our culture is constantly energized by new and exciting growth opportunities and at a rapid pace. Below are details on an open job. If the role interests you and you would like to be considered we encourage you to apply!

The role in one line:

Lead a data science team that turns Scotts' commercial questions into production ML and analytics, pricing and elasticities, demand and POS forecasting, category and audience insight, and the models that power our AI agents, using agentic practices to move faster while holding a high bar for ML rigor and engineering discipline.

Why this role is different here

Your models ship, they don't die in notebooks. Data Science sits inside the same org as the build and agent engine, so the work goes into production agents and applications, not slide decks.
Agentic-first, with judgment where it counts. The team uses AI agents and coding assistants to absorb the formulaic ~45% of data science work (profiling, EDA, feature scaffolding, hyperparameter search, monitoring checks) so people spend their time on method, interpretation, and business impact.
You focus on modeling and impact, not plumbing. A dedicated Data Engineering function owns the data foundation (pipelines, ingestion, the lakehouse), so your team builds on solid ground.

What you will own

The ML and analytics portfolio: price and promotion elasticities, POS and demand forecasting, category and market analysis, audience and activation analytics, and the models that feed our AI agents.
The team: lead, coach, and grow a group of data scientists and senior analysts; set technical standards; hire for the net-new skills as the function scales.
The bar: a mature, reproducible ML lifecycle across the team, from experiment to production to monitoring to retirement.
The business link: a clear, measurable connection between the team's models and outcomes (forecast accuracy, margin, conversion, revenue), and the ability to tell that story to non-technical partners.

What you will do

Data science and ML delivery

Frame ambiguous business questions as tractable modeling problems; choose the right method and know its limits.
Deliver models across the relevant families: time-series forecasting, causal and elasticity modeling, regression and classification, gradient-boosted trees, and modern ML as appropriate.
Set the standard for evaluation: define success metrics and golden datasets up front, and hold models (and agent-assisted analysis) to them. Eval-driven development is the default.

Agentic practice in data science

Put AI coding and analysis agents (for example Cursor, Claude Code, and notebook or pipeline agents) into the team's daily workflow to automate repetitive work and compress cycle time.
Build agent-assisted workflows for EDA, data profiling, feature engineering, hyperparameter search, and monitoring, with human review at the decision points.
Apply sound judgment on where to trust a model and where to ground or verify it; teach the team to do the same.

Engineering rigor and MLOps

Treat models as production software: reproducible pipelines, version control, testing, and clean, reviewable code.
Own CI/CD for ML, model and data versioning, lineage tracking, staged rollouts, drift and performance monitoring, retraining triggers, rollback, and model governance.
Package models as services and APIs so they integrate cleanly into agents and applications.

Leadership and partnership

Coach and develop the team; recruit and level talent; set a culture of rigor, speed, and continuous learning.
Sequence work with business Product Owners; manage dependencies with Data Engineering and the build teams.
Communicate impact and tradeoffs clearly to technical and business audiences.

Must-have qualifications:

Strong ML foundations. Solid grounding in ML algorithms and statistics, able to select, tune, and critique methods (forecasting, causal/elasticity modeling, boosting, classical ML), not just call libraries.
ML engineering. Production-quality Python; reproducible pipelines; fluent with Git, testing, containers, and APIs.
MLOps, CI/CD, and versioning. 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.
Agentic fluency. Confident daily use of AI coding and analysis tools; working understanding of LLM evaluation, RAG, embeddings, vector search, and agent workflows, including their failure modes.
People leadership. Track record leading and growing a data science team, coaching individuals, and prioritizing across competing stakeholders.
Business acumen. Demonstrated ability to tie modeling work to measurable business outcomes and to explain it to non-technical leaders.

Nice to have:

CPG, retail, or commercial analytics experience: pricing and promotion, POS and syndicated data (Amazon, retailer POS), category and shopper analytics.
Databricks and Google Cloud (BigQuery, Vertex AI, GKE); GitLab.
Experience feeding models into agent platforms or LLM-based systems.
Advanced degree in a quantitative discipline, or equivalent applied experience.

The starting budgeted pay range for this role will generally fall between $175,700.00 - $206,700.00 per year. Scotts will consider various factors in determining the actual pay including your skills, qualifications, experience, and geographical location.In addition to the determined base salary, this role is also incentive eligible under our corporate bonus programs.For remote roles where the final candidate resides in Alaska, California, Colorado, Illinois, New York, Oregon or Washington, state required pay thresholds will be factored into base salary.

Here at ScottsMiracle-Gro, we believe providing an enriching and engaging employee experience is what sets us apart from other organizations. We recognize our employees are so much more than just their job title so we offer programs and benefits that support them in all aspects of their lives. Wondering how we do it? Below is a glimpse of our highlight reel…

Our Live Total Health program provides you with options to align to your personal needs . Selections range from medical, dental and vision coverage for you, your spouse/domestic partner and dependents to an outstanding wellness reimbursement program to an unbelievable 401K match (up to 7.5%) as well as a 15% discount on company stock and much more
We know our talent is our most precious asset and your unique development contributes to our organization’s success now and in the future. Career growth at our company is not always a ladder. It’s much more like a rock climbing adventure. Grow through exploration and experiences rather than a predictable linear path.
We value the importance of family . We provide access to Maven Family Planning and up to $30,000 to accommodate for adoption, fertility and surrogacy .
Be part of something bigger by joining one of our Employee Resource Groups focusing on diversity and inclusion, family, education and sustainability : Scotts Women’s Network, Scotts Black Employees’ Network, Scotts Veterans Network, Scotts Young Professionals, Scotts Pride Network (GroPride), Scotts Associates for a Greener Earth (SAGE), Scotts Family TREE and our Associate Boards.
Join a company with a strong belief in giving bac k to the communities where we live and work. We have a shared passion for service and volunteerism and believe participating in community service benefits our communities and strengthens our team.

Not interested in this role? Stay up to date on future opportunities by joining our ScottsMiracle-Gro and Hawthorne Gardening talent communities.

Scotts is an EEO Employer, dedicated to a culturally diverse, drug free workplace.

EEO/AA Employer/Minority/Female/Disability/Veteran/Sexual Orientation/Gender Identity

Notification to Agencies :

Please note that the Scotts Miracle-Gro company does not accept unsolicited resumes from recruiters or employment agencies. In the absence of a signed Master Service Agreement, and specific approval to submit resumes to an approved requisition, the Scotts Miracle-Gro company will not consider or approve payment regarding recruiter fees or referral compensations.

Apply Now

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part time library technical assistant jobs in columbus

Physician Clinical Faculty, Radiation Oncologist-GU (Open Rank/Track Faculty)

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The role in one line:

Why this role is different here

Your models ship, they don't die in notebooks. Data Science sits inside the same org as the build and agent engine, so the work goes into production agents and applications, not slide decks.

Agentic-first, with judgment where it counts. The team uses AI agents and coding assistants to absorb the formulaic ~45% of data science work (profiling, EDA, feature scaffolding, hyperparameter search, monitoring checks) so people spend their time on method, interpretation, and business impact.

You focus on modeling and impact, not plumbing. A dedicated Data Engineering function owns the data foundation (pipelines, ingestion, the lakehouse), so your team builds on solid ground.

What you will own

The ML and analytics portfolio: price and promotion elasticities, POS and demand forecasting, category and market analysis, audience and activation analytics, and the models that feed our AI agents.

The team: lead, coach, and grow a group of data scientists and senior analysts; set technical standards; hire for the net-new skills as the function scales.

The bar: a mature, reproducible ML lifecycle across the team, from experiment to production to monitoring to retirement.

The business link: a clear, measurable connection between the team's models and outcomes (forecast accuracy, margin, conversion, revenue), and the ability to tell that story to non-technical partners.

What you will do

Data science and ML delivery

Frame ambiguous business questions as tractable modeling problems; choose the right method and know its limits.

Deliver models across the relevant families: time-series forecasting, causal and elasticity modeling, regression and classification, gradient-boosted trees, and modern ML as appropriate.

Set the standard for evaluation: define success metrics and golden datasets up front, and hold models (and agent-assisted analysis) to them. Eval-driven development is the default.

Agentic practice in data science

Put AI coding and analysis agents (for example Cursor, Claude Code, and notebook or pipeline agents) into the team's daily workflow to automate repetitive work and compress cycle time.

Build agent-assisted workflows for EDA, data profiling, feature engineering, hyperparameter search, and monitoring, with human review at the decision points.

Apply sound judgment on where to trust a model and where to ground or verify it; teach the team to do the same.

Engineering rigor and MLOps

Treat models as production software: reproducible pipelines, version control, testing, and clean, reviewable code.

Own CI/CD for ML, model and data versioning, lineage tracking, staged rollouts, drift and performance monitoring, retraining triggers, rollback, and model governance.

Package models as services and APIs so they integrate cleanly into agents and applications.

Leadership and partnership

Coach and develop the team; recruit and level talent; set a culture of rigor, speed, and continuous learning. Sequence work with business Product Owners; manage dependencies with Data Engineering and the build teams. Communicate impact and tradeoffs clearly to technical and business audiences.

Coach and develop the team; recruit and level talent; set a culture of rigor, speed, and continuous learning.

Sequence work with business Product Owners; manage dependencies with Data Engineering and the build teams.

Communicate impact and tradeoffs clearly to technical and business audiences.

Must-have qualifications:

Strong ML foundations. Solid grounding in ML algorithms and statistics, able to select, tune, and critique methods (forecasting, causal/elasticity modeling, boosting, classical ML), not just call libraries.

ML engineering. Production-quality Python; reproducible pipelines; fluent with Git, testing, containers, and APIs.

MLOps, CI/CD, and versioning. 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.

Agentic fluency. Confident daily use of AI coding and analysis tools; working understanding of LLM evaluation, RAG, embeddings, vector search, and agent workflows, including their failure modes.

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

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

Nice to have:

CPG, retail, or commercial analytics experience: pricing and promotion, POS and syndicated data (Amazon, retailer POS), category and shopper analytics.

Databricks and Google Cloud (BigQuery, Vertex AI, GKE); GitLab.

Experience feeding models into agent platforms or LLM-based systems.

Advanced degree in a quantitative discipline, or equivalent applied experience.

Coach and develop the team; recruit and level talent; set a culture of rigor, speed, and continuous learning.
Sequence work with business Product Owners; manage dependencies with Data Engineering and the build teams.
Communicate impact and tradeoffs clearly to technical and business audiences.