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Data Science Manager Jobs in Kansas (NOW HIRING)

StackAdapt

$91K - $123K/yr

The team works closely with product managers, backend engineers, web engineers, data scientists, analysts, business teams, and customer-facing teams to make data accurate, timely, scalable, and easy ...

Truity Credit Union

Data & AI Engineer

Lawrence, KS

$104K - $125K/yr

... management and AI enablement capabilities. This role focuses on automating data workflows ... Bachelor's degree in Data Science, Computer Science, or related technical field. * 5+ years of ...

Truity Credit Union

Data & AI Engineer

Kansas City, KS

$104K - $125K/yr

... management and AI enablement capabilities. This role focuses on automating data workflows ... Bachelor's degree in Data Science, Computer Science, or related technical field. * 5+ years of ...

Tyler Technologies, Inc.

Data Engineer

Overland Park, KS · On-site

$113K - $135K/yr

Description The data engineer provides technical data support to cross functional teams who are ... Bachelor's degree preferred, or equivalent, in Computer Science, Management Information Systems or ...

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Data Science Manager information

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$27.6K

$86.6K

$153.4K

How much do data science manager jobs pay per year?

As of Jul 13, 2026, the average yearly pay for data science manager in Kansas is $86,638.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,900.00 and $111,900.00 per year, depending on experience, location, and employer.

What are the primary responsibilities of a Data Science Manager on a day-to-day basis?

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 is a Data Science Manager job?

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.

Is 40 too late for data science?

Age is not a barrier to becoming a data science manager; many professionals transition into data science roles later in their careers. Success depends on relevant skills, experience, and continuous learning in areas like programming, statistics, and machine learning. Employers value diverse backgrounds and practical expertise regardless of age.

What is the 80 20 rule in data science?

The 80/20 rule in data science suggests that roughly 80% of results come from 20% of the efforts or data. Data scientists often use this principle to focus on the most impactful features, models, or data subsets to improve efficiency and outcomes in projects.

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 between technical staff and business stakeholders, often requiring skills in leadership, communication, and familiarity with tools like Python, R, or SQL. Their responsibilities include managing workflows, mentoring team members, and ensuring project deliverables align with organizational objectives.

How much do data scientist managers make?

Data Science Managers typically earn between $110,000 and $160,000 annually, with salaries varying based on experience, location, and company size. They often oversee teams of data scientists, coordinate projects, and require strong skills in analytics tools and leadership. Senior roles or those in high-cost areas can offer higher compensation.

What are the key skills and qualifications needed to thrive in the Data Science Manager position, and why are they important?

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 are the most commonly searched types of Data Science jobs in Kansas? The most popular types of Data Science jobs in Kansas are:
What are popular job titles related to Data Science Manager jobs in Kansas? For Data Science Manager jobs in Kansas, the most frequently searched job titles are:
What job categories do people searching Data Science Manager jobs in Kansas look for? The top searched job categories for Data Science Manager jobs in Kansas are:
Data Science Manager jobs near you
Infographic showing various Data Science Manager job openings in Kansas as of July 2026, with employment types broken down into 1% As Needed, 87% Full Time, 10% Part Time, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $86,638 per year, or $41.7 per hour.

Senior Staff Software Engineer, Data Delivery

StackAdapt

On-site

$91K - $123K/yr

Other

Re-posted 5 days ago

Job description

About the team

The Data Delivery team builds the systems that help StackAdapt customers and internal teams understand campaign performance.

We process, organize, and serve large volumes of campaign and marketing data so it can be used reliably across customer-facing dashboards, reporting, billing, forecasting, campaign pacing, measurement, exports, and internal analytics.

Our work turns raw activity - such as impressions, clicks, conversions, spend, pacing, audience reach, creative performance, and cross-channel engagement - into accurate and actionable insights.

The team works closely with product managers, backend engineers, web engineers, data scientists, analysts, business teams, and customer-facing teams to make data accurate, timely, scalable, and easy to use.

Why this role matters

StackAdapt is expanding into the next generation of AI-powered advertising and marketing execution. As the platform grows across media, email, social, and emerging AI-powered channels, customers need a trusted view of performance across the full customer journey.

The Data Delivery team is at the forefront of making this possible. We build the reporting, measurement, forecasting, billing, API, export, and data-serving systems that help customers trust the data, understand performance, and optimize campaigns.

As a Senior Staff Software Engineer on Data Delivery, you will help shape the long-term technical strategy for Data Delivery and the broader Stats & Analytics domain. You will work across teams to understand how the platform fits together, identify the right technical investments, and enable new product and feature development through scalable, reliable, and well-designed data systems.

This role is ideal for someone who can operate across a broad technical surface area, connect product goals with platform architecture, and influence technical direction beyond a single team.

To learn more about StackAdapt's broader platform vision, watch Yang Han discuss the evolution of StackAdapt's AI-powered advertising and marketing platform:
How AI Advertising Platform StackAdapt Connects Marketers With Agencies - CTO Yang Han

What you'll be doing
  • Technical strategy: Shape the long-term architecture and technical direction for Data Delivery and related Stats & Analytics systems.
  • Cross-team leadership: Lead initiatives that span multiple teams, systems, and product areas, helping teams align on durable technical solutions.
  • Platform enablement: Build and guide systems that make it easier to launch new reporting, measurement, forecasting, billing, export, and analytics capabilities.
  • System design: Make high-impact technical decisions across APIs, data pipelines, data models, distributed systems, storage, serving layers, and operational patterns.
  • Product partnership: Work with product, engineering, data science, analytics, and business leaders to translate company priorities into technical roadmaps.
  • Technical quality: Identify systemic gaps in reliability, scalability, data quality, developer experience, and operational ownership, then drive improvements.
  • Decision-making: Clarify trade-offs around correctness, latency, freshness, cost, migration complexity, maintainability, and long-term platform leverage.
  • Technical mentorship: Coach and mentor engineers, including senior engineers, through architecture reviews, design discussions, and strategic technical guidance.
What you'll bring
  • Broad technical leadership: Experience shaping technical direction across teams, domains, or large platform areas.
  • Backend and data systems expertise: Deep experience building scalable services, distributed systems, data platforms, or data-intensive applications.
  • Strategic system design: Strong judgment across APIs, data pipelines, databases, distributed systems, observability, reliability, and long-term architecture.
  • Cross-functional influence: Ability to align engineering, product, data science, analytics, and business stakeholders around technical strategy.
  • Platform thinking: Ability to design systems that create leverage for multiple teams and make future product development easier.
  • Data quality mindset: Strong ability to reason about correctness, freshness, completeness, consistency, explainability, cost efficiency, and customer trust.
  • Business and product judgment: Ability to connect technical investments to customer impact, product velocity, reliability, and long-term business value.
  • Technical communication: Ability to explain complex technical trade-offs clearly to both technical and non-technical audiences.
  • Mentorship: Experience developing senior engineers and raising engineering standards across a team or domain.
  • Technical stack: Strong programming skills; experience with Golang and technologies such as Kafka, TiDB, Redshift, Vitess, Iceberg, StarRocks, or Trino is a plus.
  • Domain experience: Experience in adtech, marketing technology, reporting, analytics, billing, attribution, forecasting, or high-volume event processing is a plus.
What success looks like

You will help Data Delivery and the broader Stats & Analytics domain build trusted, scalable, and efficient data systems that enable faster product development and better customer outcomes.

In practice, this means:

  • Data Delivery has a clear long-term technical direction that supports StackAdapt's product and platform growth.
  • Teams can launch new reporting, measurement, forecasting, billing, export, and analytics capabilities faster and with greater confidence.
  • Core data systems are more reliable, scalable, cost-efficient, and easier to evolve.
  • Product and engineering teams have a stronger understanding of how Stats & Analytics systems fit together.
  • Technical trade-offs are clearly understood, documented, and aligned across teams.
  • Engineers around you grow through your mentorship, strategic guidance, and example.
  • Data Delivery helps StackAdapt scale new advertising and marketing opportunities by giving customers a trusted view of performance across channels and the customer journey.

Apply