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Senior Director Data Science Jobs in Ohio (NOW HIRING)

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Director Data & Insights

Columbus, OH

... data science, and reporting and analytics. KEY RESPONSIBILITIES Organizational Strategy & Vision ... Partner with senior leadership to shape strategic pillars and modernize the data stack ...

Express

Director Data & Insights

Columbus, OH · On-site

... science, and reporting and analytics. KEY RESPONSIBILITIES * Organizational Strategy & Vision ... Partner with senior leadership to shape strategic pillars and modernize the data stack ...

Express

Director Data & Insights

Columbus, OH

... science, and reporting and analytics. KEY RESPONSIBILITIES * Organizational Strategy & Vision ... Partner with senior leadership to shape strategic pillars and modernize the data stack ...

GRVTY

Data Science - Senior

Dayton, OH · On-site

Advanced degree in Computer Science, Data Science, Electrical Engineering, or related field, or equivalent experience. * Strong proficiency in Python (PEP8, Google Style Guide, type hints, docstring ...

GRVTY

Data Science - Senior

Dayton, OH · On-site

Advanced degree in Computer Science, Data Science, Electrical Engineering, or related field, or equivalent experience. * Strong proficiency in Python (PEP8, Google Style Guide, type hints, docstring ...

TQL

Senior Data Scientist

Cincinnati, OH · On-site

Opportunity to shape data science strategy and standards* Direct collaboration with senior stakeholders* Leadership and mentorship opportunities* Comprehensive benefits package with health, dental ...

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Senior Director Data Science information

What does a senior director of data science do?

A Senior Director of Data Science leads and oversees the data science strategy for an organization, managing teams of data scientists, analysts, and engineers. They are responsible for aligning data initiatives with business goals, guiding advanced analytics projects, and ensuring the effective use of data to drive decision-making. This role often involves collaborating with other executives to develop data-driven solutions, establishing best practices, and setting the vision for how data science supports organizational growth.

How does a senior director of data science typically collaborate with other departments within an organization?

A Senior Director of Data Science frequently partners with leaders from product, engineering, marketing, and business strategy to align data-driven insights with organizational goals. They facilitate cross-functional collaboration by translating complex analytics into actionable business recommendations, ensuring that data science initiatives support top-level priorities. This role often leads a team of data scientists while serving as a bridge between technical teams and non-technical stakeholders, fostering a culture of data-informed decision-making throughout the company.

What are the key skills and qualifications needed to thrive as a senior director data science, and why are they important?

To thrive as a Senior Director Data Science, you need deep expertise in advanced analytics, machine learning, statistical modeling, and a strong educational background in a quantitative field, often with a master's or PhD. Familiarity with data platforms (like AWS, Azure), programming languages (such as Python, R), and leadership in deploying enterprise-level data solutions is vital, along with experience managing large teams. Exceptional strategic thinking, communication, and stakeholder management skills set top candidates apart in this role. These abilities are crucial for driving data-driven business strategies, leading high-performing teams, and ensuring impactful outcomes at the organizational level.

What is the difference between Senior Director Data Science vs Data Science Manager?

AspectSenior Director Data ScienceData Science Manager
ResponsibilitiesOversees multiple teams, sets strategic vision, aligns data science initiatives with business goalsManages day-to-day operations of data science teams, executes projects, and ensures deliverables
Required CredentialsAdvanced degree (Master's/PhD), extensive experience, leadership skillsRelevant degree, experience in managing data projects, technical expertise
Work EnvironmentStrategic, cross-departmental, executive collaborationOperational, team-focused, project management

The Senior Director Data Science typically holds a higher strategic leadership role, overseeing multiple teams and aligning data initiatives with company goals. In contrast, a Data Science Manager focuses on managing teams and executing projects. Both roles require strong technical backgrounds, but the Senior Director emphasizes strategic vision and leadership across departments.

What are popular job titles related to Senior Director Data Science jobs in Ohio?

For Senior Director Data Science jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Senior Director Data Science jobs in Ohio look for?

The top searched job categories for Senior Director Data Science jobs in Ohio are:

What cities in Ohio are hiring for Senior Director Data Science jobs?

Cities in Ohio with the most Senior Director Data Science job openings:

Senior Director Data Science jobs near you
Infographic showing various Senior Director Data Science job openings in Ohio as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 11% Part Time, 1% Temporary, and 4% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Sr. Manager Data Science

The Scotts Company LLC

Marysville, OH • On-site

Full-time

Medical, Dental, Retirement

Re-posted 11 days ago

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. 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 visioncoverage 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 ourtalent 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.

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

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