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Data Scientist Project Manager Jobs in Kansas (NOW HIRING)

Nordic

Strong communication and stakeholder management skills (required) * Masters, Data Science, Computer Science, Engineering, or Statistics (preferred) * Doctorate, Data Science, Computer Science ...

TAMKO

Manufacturing Data Scientist

Phillipsburg, KS · On-site

Key Responsibilities: * Assist in the scoping, execution, and completion of projects that align ... Present findings, prototypes, and recommendations to peers, managers, operators, and executives ...

Amgen

Sr. Data Scientist - Process Modeling

Wichita, KS · On-site

Data Scientist - Process Modeling What you will do Let's do this. Let's change the world. In this ... Change management to integrate tech transfer digital applications into product teams * Development ...

Ascend Learning

Sr Data & AI Scientist

Leawood, KS · On-site +1

$151K - $215K/yr

A minimum of 7 years' experience working on data science, machine learning, and artificial intelligence projects in industry, with a strong and growing emphasis on applied AI and generative AI/large ...

Reply

Manager, Data Engineering

Kansas City, KS · On-site

... data science projects while liaising with clients to meet their needs * Providing leadership ... Collaborating with project management to plan work for delivery teams and verifying quality ...

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Data Scientist Project Manager information

What is a data scientist project manager?

A Data Scientist Project Manager is a professional who oversees data science projects from conception through completion, ensuring that project goals align with business objectives. They bridge the gap between data science teams and stakeholders, managing timelines, resources, and communication. In addition to technical knowledge in data science and analytics, they possess strong project management skills to coordinate tasks, mitigate risks, and deliver results. Their role is essential for translating complex data-driven insights into actionable business strategies. They often use methodologies like Agile or Scrum to guide project workflows and adapt to changing requirements.

What are the key skills and qualifications needed to thrive as a data scientist project manager?

To thrive as a Data Scientist Project Manager, you need a solid background in data science, analytics, and project management, often supported by degrees in computer science, statistics, or business and certifications like PMP or Agile. Familiarity with tools such as Python, R, SQL, project management software (e.g., Jira, Trello), and cloud platforms is crucial. Excellent communication, leadership, and problem-solving abilities help bridge gaps between technical teams and stakeholders. These skills ensure successful project delivery by aligning data-driven insights with business objectives and effective team coordination.

How do data scientist project managers typically balance technical data work with project management responsibilities?

Data Scientist Project Managers often split their time between hands-on data analysis and overseeing project progress. They commonly coordinate with cross-functional teams, set project timelines, and ensure that data solutions align with business objectives while occasionally contributing code or analytical insights. Effective communication and time management are essential, as they must bridge the gap between technical teams and stakeholders. This dual responsibility offers exposure to both technical growth and leadership development, making it ideal for professionals seeking advancement into higher management roles.

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

AspectData Scientist Project ManagerData Analyst Project Manager
Required CredentialsBachelor's/Master's in Data Science, Analytics, or related fields; certifications like PMP or AgileBachelor's in Data Analysis, Business, or related fields; certifications like PMP or Agile
Work EnvironmentLeads data science projects, collaborates with data scientists and engineersManages data analysis projects, works with analysts and business teams
Employer & Industry UsageTech companies, finance, healthcare, industries with advanced analyticsRetail, marketing, finance, industries relying on data reporting

The main difference is that Data Scientist Project Managers oversee data science initiatives involving complex modeling and algorithms, while Data Analyst Project Managers focus on managing data reporting and analysis projects. Both roles require project management skills and relevant certifications, but their technical focus and team collaboration differ.

Can a data scientist become a project manager?

A data scientist can transition to a project manager role by developing skills in leadership, communication, and project planning, often supported by certifications like PMP or Agile. Their technical expertise can be valuable in managing data-driven projects, but they typically need to gain experience in managing teams and project workflows.

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Data Scientist (Statistician) - Direct Hire

US Department of the Treasury

Topeka, KS • On-site

$125K/yr

Full-time

Posted 7 days ago

U.S. Department Of The Treasury rating

8.2

Company rating: 8.2 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

311th of 855 rated public administrative organizations

Job description

WHAT IS LARGE BUSINESS AND INTERNATIONALDIVISION?
A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions

  • Position is to be filled in the following area(s):
    • LBI - ADCCI - Assistant Deputy Commissioner Compliance Integration.


REVIEW THE ADDITIONAL INFORMATION BELOW FOR FURTHER DETAILS

Qualifications:Federal experience is not required. Experience may have been gained in the public sector, private sector or through Volunteer Service. One year of experience refers to full-time work; part-timework is considered on a prorated basis. To ensure full credit for your work experience, please indicate dates of employment by month/day/year, and indicate number of hours worked per week, on your resume.
You must meet the following requirements by the closing date of this announcement.
QUALIFICATION REQUIREMENTS: To qualify for this position, you must meet the qualification requirements outlined below:
BASIC REQUIREMENTS (IOR) ALL GRADES:
EDUCATION: A degree that included 15 semester hours in statistics (or in mathematics and statistics, provided at least 6 semester hours were in statistics), and 9 additional semester hours in one or more of the following: physical or biological sciences, medicine, education, or engineering; or in the social sciences including demography, history, economics, social welfare, geography, international relations, social or cultural anthropology, health sociology, political science, public administration, psychology, etc. Credit toward meeting statistical course requirements should be given for courses in which 50 percent of the course content appears to be statistical methods, e.g., courses that included studies in research methods in psychology or economics such as tests and measurements or business cycles, or courses in methods of processing mass statistical data such as tabulating methods or electronic data processing.
OR
COMBINATION OF EDUCATION AND EXPERIENCE: Combination of education and experience includes courses as shown in A above, plus appropriate experience or additional education. The experience should have included a full range of professional statistical work such as (a) sampling, (b) collecting, computing, and analyzing statistical data, and (c) applying statistical techniques such as measurement of central tendency, dispersion, skewness, sampling error, simple and multiple correlation, analysis of variance, and tests of significance.
AND
SPECIALIZED EXPERIENCE GS-14: In addition to meeting basic requirements, to be eligible for this position at this grade level, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-13 grade level in the Federal service.
Specialized experience for this position includes:
  • Experience identifying and assessing the validity and reliability of relevant data sources and retrieving structured and unstructured data in multiple types and formats, including Extensible Markup Language (XML) files and large datasets, for use in data science projects.
  • Experience cleaning, transforming, combining, and integrating structured and unstructured data from multiple sources, including identifying and resolving missing values, outliers, and duplicate records, to prepare data for analysis.
  • Experience applying data-mining process models, including the Cross-Industry Standard Process for Data Mining (CRISP-DM) or Sample, Explore, Modify, Model, Assess (SEMMA), to collect, prepare, analyze, and evaluate data during data science projects.
  • Experience applying statistical methods, probability, statistical inference, hypothesis testing, experimental design, forecasting, and sampling methods to analyze data, evaluate results, and support program or business decisions.
  • Experience developing and evaluating analytical and artificial intelligence models using machine learning, text analytics, natural language processing, large language models, graph theory, link analysis, optimization models, complex adaptive systems, or deep-learning neural networks.
  • Experience using programming languages, query languages, data-intelligence platforms, and data-storage technologies, including R, Python, Structured Query Language (SQL), Java, Databricks, Sybase, Oracle, or open-source databases, to retrieve, process, query, analyze, and integrate data during data science projects.
  • Experience planning, coordinating, monitoring, and evaluating data science projects; reviewing technical deliverables for validity and reliability; and communicating analytical findings, model results, limitations, conclusions, and recommendations to technical and nontechnical stakeholders through written products, presentations, graphs, tables, charts, or business-intelligence products.

AND
You must also meet the following requirements:
  • MINIMUM AGE REQUIREMENT: Minimum age for federal employment is 18 years old, or at least 16 years old and have:
    • Graduated from high school or been awarded a certificate equivalent to graduating from high school; or
    • Completed a formal vocational training program; or
    • Received a statement from school authorities agreeing with your preference for employment rather than continuing your education

For more information on qualifications please refer to OPM's Qualifications Standards.Education:A college or university degree generally must be from an accredited (or pre-accredited) college or university recognized by the U.S. Department of Education. For a list of schools which meet these criteria, please refer to Department of Education Accreditation page.
FOREIGN EDUCATION: Education completed in foreign colleges or universities may be used to meet the requirements. You must show proof the education credentials have been deemed to be at least equivalent to that gained in conventional U.S. education program. It is your responsibility to provide such evidence when applying. Click here (Section 3, Explanation of Terms) or here for Foreign Education Credentialing instructions.
We recommend choosing an evaluator from a member organization of one of the following national associations of credential evaluation services: National Association of Credential Evaluation Services (NACES) or Association of International Credentials Evaluators (AICE).Employment Type: OTHER

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