Sr. Manager Data Science

Hawthorne Gardening Co.; United States; Remote; USD 176,000 - 207,000; Full time

Benefits offered by this job

Live Total Health program

401K with company match

Stock purchase discount

Adoption assistance

Employee Resource Groups

Job summary

ScottsMiracle-Gro seeks a data science leader to guide a team turning commercial questions into production ML and analytics, pricing elasticities, demand forecasts, and audience insights. You will nurture a high-rigor ML culture and ensure models ship into production with solid engineering foundations.

You will coach data scientists and senior analysts, establish standards, and partner with Data Engineering and build teams to scale impact across the organization.

Qualifications

• Strong ML foundations and ability to critique methods (forecasting, elasticity, boosting).
• Production-quality Python; reproducible pipelines; MLOps, CI/CD, and governance at scale.
• Agentic fluency with AI tools, LLM eval, embeddings, vector search and agent workflows.

Responsibilities

• Lead and grow a data science team; set technical standards and recruit as needed.
• Own a mature ML lifecycle from experiment to production and monitoring to retirement.
• Collaborate with Data Engineering and Build teams; communicate impact to technical and business audiences.

Skills

ML foundations

ML engineering

Agentic fluency

People leadership

Business acumen

Education

Advanced degree in quantitative discipline

Tools

Python

Git

Containers

APIs

Job description

Here at ScottsMiracle-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. 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

• price and promotion elasticities
• POS and demand forecasting
• category and market analysis
• audience and activation analytics
• 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 back 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

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