Sr. Manager Data Science

Hawthorne Gardening Co.; Marysville (OH); On-site; USD 175,700 - 206,700; Full time

Job summary

Scotts Miracle-Gro in Marysville, OH is seeking a Sr. Manager Data Science to lead a team delivering production ML and analytics, pricing and elasticities, demand and POS forecasting, and models powering AI agents.

You will own the ML portfolio and coach data scientists, driving a mature ML lifecycle from experiment to production. The role collaborates with Product, Data Engineering, and analytics partners to tie modeling work to measurable business outcomes and forecast accuracy, margin, and

Qualifications

• Strong ML foundations and statistics for forecasting and modeling.
• ML engineering: production-quality Python, reproducible pipelines.
• MLOps: CI/CD, model/versioning, monitoring at scale.
• Agentic fluency with AI tools and agent workflows.
• People leadership: coach and grow a data science team.
• Business acumen: tie modeling to measurable outcomes.

Responsibilities

• Frame ambiguous business questions as tractable modeling problems.
• Deliver models across time-series forecasting, elasticity modeling, regression and classification.
• Define evaluation metrics and golden datasets up front; ensure reproducible results.
• Collaborate with Product Owners and Data Engineering to deploy into production.

Skills

ML foundations

ML engineering

MLOps

Agentic fluency

People leadership

Business acumen

Education

Advanced degree in a quantitative discipline

Tools

Databricks

Google Cloud

Job description

## Sr. Manager Data ScienceApplylocations: Marysville, OH: Ohio - Fieldtime type: Full timeposted on: Posted Todayjob requisition id: R26216Here 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.