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Data Science Project Manager Jobs in Ohio (NOW HIRING)

... projects leading to applied business results. Use advanced techniques that integrate traditional ... assessing, managing, monitoring, and reporting risks of all types. ESSENTIAL DUTIES AND ...

New

... management and application development pipeline in support of national defense data science and ... Experience leading teams and projects * Programming experience in C++ and Java * Experience with ...

New

We are looking for a Data Science Analyst who can not just report on performance, but also ... Ability to manage client expectations, present to non-technical audiences, and pivot quickly based ...

... data science projects independently while guiding other data scientists through technical expertise ... Lead cross-functional projects with occasional light project management across teams to deliver ...

... management and application development pipeline in support of national defense data science and ... Experience leading teams and projects * Programming experience in C++ and Java * Experience with ...

Ability to partner with product managers and stakeholders to translate business needs into science ... Bachelor's or Master's in Statistics, Data Science, Computer Science, Applied Math, Economics, or ...

Build and manage datasets for training and evaluation in collaboration with subject matter experts ... Advanced degree in Computer Science, Data Science, Electrical Engineering, or related field, or ...

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Data Science Project Manager information

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$15

$54

$76

How much do data science project manager jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for data science project manager in Ohio is $54.67, according to ZipRecruiter salary data. Most workers in this role earn between $47.31 and $63.99 per hour, depending on experience, location, and employer.

What is a data science project manager?

A Data Science Project Manager is a professional who oversees and coordinates data science projects from inception to completion. They act as a bridge between technical data science teams and business stakeholders, ensuring that project goals align with organizational objectives. Responsibilities include planning project timelines, managing resources, mitigating risks, and communicating progress. They also help define project requirements, monitor deliverables, and ensure that outcomes meet quality standards. Strong communication, analytical, and organizational skills are essential for this role.

How does a data science project manager typically collaborate with data scientists and stakeholders throughout a project?

A Data Science Project Manager acts as a bridge between technical teams and business stakeholders, ensuring clear communication of goals, timelines, and deliverables. They facilitate regular meetings to discuss project progress, address any obstacles, and realign priorities as needed. By translating business requirements into actionable tasks for data scientists and providing updates to stakeholders, they help ensure that projects stay on track and deliver value. Effective collaboration often involves balancing technical feasibility with business needs, managing expectations, and fostering a cooperative team environment.

What is the difference between Data Science Project Manager vs Data Analyst?

AspectData Science Project ManagerData Analyst
Required CredentialsOften requires a bachelor’s or master’s in data science, analytics, or related fields; project management certifications beneficialTypically holds a bachelor’s degree in statistics, mathematics, or related areas; certifications like Microsoft Excel or Tableau are common
Work EnvironmentLeads data science projects, collaborates with data scientists, engineers, and stakeholdersAnalyzes data sets, creates reports, visualizations, and supports decision-making
Employer & Industry UsageUsed in tech, finance, healthcare, and consulting firms managing data science initiativesFound across industries for data reporting, business intelligence, and operational analysis

In summary, a Data Science Project Manager oversees data science projects and manages teams, requiring project management skills and relevant certifications. A Data Analyst focuses on analyzing data and creating reports, with a more technical and analytical role. Both roles are essential in data-driven organizations but differ in scope and responsibilities.

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

To thrive as a Data Science Project Manager, you need a solid understanding of data science methodologies, project management principles, and usually a degree in computer science, statistics, or a related field. Familiarity with analytics tools (such as Python, R, SQL), project management software (like Jira or Trello), and certifications such as PMP or Agile/Scrum are often required. Strong leadership, communication, and problem-solving skills set top performers apart by enabling effective team coordination and stakeholder management. These competencies ensure projects are delivered on time, within scope, and generate actionable insights that drive business value.
What are popular job titles related to Data Science Project Manager jobs in Ohio? For Data Science Project Manager jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Data Science Project Manager jobs in Ohio look for? The top searched job categories for Data Science Project Manager jobs in Ohio are:
What cities in Ohio are hiring for Data Science Project Manager jobs? Cities in Ohio with the most Data Science Project Manager job openings:
Infographic showing various Data Science Project Manager job openings in Ohio as of August 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution, with an average salary of $113,719 per year, or $54.7 per hour.

Full-time

Medical, Dental, Vision, Retirement

Posted 18 days ago


ScottsMiracle-Gro rating

7.4

Company rating: 7.4 out of 10

Based on 59 frontline employees who took The Breakroom Quiz

294th of 537 rated manufacturers


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 doData 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.

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.


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