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Data Science Manager Jobs in Riverside, MO (NOW HIRING)

FanThreeSixty

Lead Data Product Manager

Leawood, KS

Data Science roadmap prioritization Own prioritization of the Data Science roadmap - deciding what ... A working cadence with the Lead Platform Product Manager where handoffs (data interface) happen ...

Pwc

AI & GenAI Data Scientist - Manager

Kansas City, MO

$99K - $232K/yr

Management Information Systems, Computer and Information Science, Systems Engineering, Mathematics ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

NinjaOne

Senior Data Engineer

Kansas, KS · On-site +1

$93K - $127K/yr

... and data science. * Demonstrated business impact and growth in prior roles * Expertise in Python and SQL. * Experience building ETL pipelines and managing large-scale data systems. * Strong ...

Reply

Manager, Data Engineering

Kansas City, MO

Bachelor's degree in computer science or related field * 12 years of industry experience, with 4 most recent years as a hands-on manager leading a team on enterprise scale data platform projects * 4 ...

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Data Science Manager information

See Riverside, MO salary details

$30K

$93.9K

$166.3K

How much do data science manager jobs pay per year?

As of Sep 13, 2026, the average yearly pay for data science manager in Riverside, MO is $93,914.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,800.00 and $121,300.00 per year, depending on experience, location, and employer.

What is a data science manager?

A Data Science Manager leads a team of data scientists to develop and implement data-driven solutions for business challenges. They oversee project timelines, ensure the quality of data analysis, and collaborate with cross-functional teams to drive decision-making. In addition to technical expertise, they require strong leadership, communication, and strategic thinking skills. Their role bridges the gap between data science initiatives and business objectives, ensuring the team's work aligns with company goals.

What does a data science manager do?

As a Data Science Manager, your daily responsibilities typically include overseeing a team of data scientists and analysts, setting project priorities, and ensuring the timely delivery of data-driven solutions. You will often collaborate with cross-functional teams, such as engineering, product, and business stakeholders, to define problems, scope solutions, and communicate analytical insights. Your role also involves mentoring team members, reviewing code and analysis, and driving best practices in data science methodologies. This position requires balancing technical project oversight with team leadership and strategic business alignment.

What skills and qualifications are needed to be a data science manager?

To thrive as a Data Science Manager, you need strong analytical skills, experience in machine learning and data analytics, and a background in statistics or computer science, often supported by an advanced degree. Familiarity with tools like Python, R, SQL, cloud platforms, and experience managing data science projects are highly valued, and certifications such as Certified Analytics Professional (CAP) can be advantageous. Excellent leadership, project management, and communication skills are crucial for guiding teams and translating technical findings for stakeholders. These abilities ensure effective team performance, successful project delivery, and the alignment of data science initiatives with organizational goals.

What is the role of a data science manager?

A data science manager oversees data science teams, guiding project priorities, setting strategic goals, and ensuring the effective use of data analysis and modeling techniques. They coordinate with stakeholders, manage resources, and often have expertise in programming, statistical methods, and data tools to support decision-making processes.

What are the most commonly searched types of Data Science jobs in Riverside, MO?

The most popular types of Data Science jobs in Riverside, MO are:

What cities near Riverside, MO are hiring for Data Science Manager jobs?

Cities near Riverside, MO with the most Data Science Manager job openings:

Data Science Manager jobs near you
Infographic showing various Data Science Manager job openings in Riverside, MO as of August 2026, with employment types broken down into 87% Full Time, 12% Part Time, and 1% Contract. Highlights an 79% Physical, 3% Hybrid, and 18% Remote job distribution, with an average salary of $93,914 per year, or $45.2 per hour.

Lead Data Product Manager

Leawood, KS

FanThreeSixty
11 - 50 employees
Learn more about FanThreeSixtyexternal

Full-time

Posted 19 days ago

Job description

Job Description Summary

FanThreeSixty is looking for a Lead Data Product Manager to own the roadmap and prioritization for our data platform's core engine - the integrations, data science models, and insights/reporting capabilities that power a leading fan engagement platform in sports and entertainment. This role sits at the intersection of data strategy and product management: you'll define what data enters and leaves our platform, guide the models and algorithms that turn raw fan data into actionable intelligence, and ensure the insights we surface are accurate, meaningful, and built to scale.
This role reports to the Sr. Director, Product & Data Strategy.
You'll work as a peer to our Lead Platform Product Manager, with a clear division of ownership: you own everything up to the point where data is consumed - the pipelines, the models, the logic, the metric definitions. Platform owns everything a client sees and clicks. Together, you'll ensure that what gets built is both analytically sound and genuinely usable.

Job Description

What You'll Own

Integrations roadmap

Prioritize and manage the roadmap for data flowing into and out of the platform, partnering with engineering to sequence integration work against business impact.

Data Science roadmap prioritization

Own prioritization of the Data Science roadmap - deciding what's worth building as a durable capability versus what should be declined or redirected as a one-off request. Translate business questions into model requirements and ensure outputs are interpretable and defensible.

Insights & reporting standards

Define what gets measured, how it's calculated, and what it means - producing clear metric definitions and requirements that downstream teams (including design and platform) build against.

Data quality & technology governance

Partner with engineering and data science to surface and prioritize data quality issues that affect model or reporting reliability. Ensure any infrastructure, tooling, or architecture decision originating from Data Science routes through Tech & Architecture's standard review process, rather than being made independently.

Internal cross-functional translation

Serve as the primary internal bridge between data science/engineering execution and product/business leadership (Sr. Director, Lead Platform Product Manager, executive leadership), translating technical tradeoffs into business terms and vice versa. Client-facing translation of data needs and use cases is owned by the Client Data Strategist - this role's translation work stays internal.

Continuous improvement

Regularly reassess whether our data architecture, models, and reporting standards still fit our clients' evolving needs and the broader industry landscape. No part of the roadmap should be treated as "done" - only as a baseline to keep improving.

What Success Looks Like

  • A prioritized, well-justified roadmap for data integrations, models, and reporting that engineering can execute against without ambiguity.
  • A measurable reduction in ad hoc, one-off reporting requests going to Data Science, replaced by reusable data products tied to business outcomes.
  • Zero Data Science infrastructure or tooling decisions made outside the standard Tech & Architecture review process.
  • Metric and model definitions that are documented, defensible, and don't require re-litigation every time they're used in a client-facing context.
  • A working cadence with the Lead Platform Product Manager where handoffs (data interface) happen smoothly, without escalation.
  • A track record of proactively identifying where our data capabilities need to evolve - not just reacting to requests, but anticipating where the platform needs to go next.

What You Bring

  • 5-7 years of product management experience, with meaningful time spent owning data-intensive products, platforms, or data science-adjacent roadmaps.
  • Demonstrated ability to write clear requirements for machine learning or statistical models - you don't need to build them, but you need to speak the language well enough to spec them.
  • Experience translating raw data/model outputs into metrics and insights that non-technical stakeholders can act on.
  • Comfort operating in ambiguity typical of a lean, high-ownership organization - this isn't a role with a large PM bench around you.
  • Strong cross-functional collaboration skills, especially the ability to hold a firm line on analytical accuracy while remaining a good partner to design and engineering.
  • Demonstrated ability to set priorities and say no to low-value work - comfort pushing back on requests that don't tie to a clear business outcome.

Nice to Have

  • Experience in sports, entertainment, or fan/customer engagement platforms.
  • Familiarity with modern data stacks (cloud data warehousing, ETL/integration tooling).
  • Background partnering directly with data science teams on production ML systems.
  • Working knowledge of data privacy and compliance considerations sufficient to partner effectively with the Privacy & Compliance Coordinator - this role is not the primary owner of compliance monitoring or interpretation.

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