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Remote Data Analytics Project Manager Jobs in Illinois

Remote, US Only About the Role: MustardSeed is seeking a PMO Data Analyst to help turn project data, client artifacts, schedules, risk logs, vendor performance history, and lessons learned into ...

Lead data and analytics projects from initiation through delivery, managing scope, timelines ... Join Collectiv for a rewarding career in a fully remote work environment with quarterly in person ...

Data Analyst

Chicago, IL ยท On-site +1

$80K/yr

Data & Automation | Remote / Hybrid | Full-Time - $80,000 base + 5-8% performance bonus The Role ... Flag data anomalies, system discrepancies, or mapping gaps proactively * Assist Project Managers ...

We have a JOB FOR YOU!!! 100% Remote and responsible for delivery of Projects Management to our ... Must have experience with Data Analytics (using tools such as Tableau, OBIEE, or Power BI)

Client Services Project Manager

Chicago, IL ยท Remote

$90K - $115K/yr

Internally, they coordinate with our sales representatives, data analysts, web developers, call ... LI-Remote The Compensation range for this role is $90,000 - $115,000 USD annually and may be ...

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Remote Data Analytics Project Manager information

What are the key skills and qualifications needed to thrive as a Remote Data Analytics Project Manager, and why are they important?

To thrive as a Remote Data Analytics Project Manager, you need a solid background in data analytics, project management methodologies, and typically a bachelor's degree in a related field. Familiarity with analytics tools (like Tableau, Power BI), project management software (such as Jira or Asana), and certifications like PMP or Agile are highly beneficial. Exceptional communication, problem-solving, and leadership skills help coordinate remote teams and manage stakeholder expectations. These skills ensure that projects are delivered on time, data-driven insights are actionable, and teams remain aligned despite working remotely.

What is a Remote Data Analytics Project Manager?

A Remote Data Analytics Project Manager oversees data analytics projects while working from a remote location, often coordinating virtual teams and managing project timelines, resources, and deliverables. They bridge the gap between technical data teams and business stakeholders, ensuring that analytics solutions align with business goals. These professionals are responsible for project planning, risk management, communication, and quality assurance throughout the project lifecycle. Their role requires strong leadership, analytical skills, and proficiency with data analytics tools and project management methodologies.

How do Remote Data Analytics Project Managers effectively coordinate with distributed analytics teams?

Remote Data Analytics Project Managers rely on robust communication tools and clear project management processes to ensure all team members stay aligned, regardless of location. They frequently schedule virtual meetings, set up centralized dashboards for tracking progress, and use collaboration platforms to share updates and resolve issues quickly. Building strong relationships with data analysts, engineers, and stakeholders is key to anticipating challenges and maintaining transparency throughout the project lifecycle. This approach helps ensure deadlines are met and project goals are achieved, even in a remote environment.

What is the difference between Remote Data Analytics Project Manager vs Data Analyst?

AspectRemote Data Analytics Project ManagerData Analyst
Required CredentialsBachelor's in Data Science, Business, or related field; PMP or similar certificationsBachelor's in Statistics, Data Science, or related field; often no certification required
Work EnvironmentLeads projects, collaborates with teams remotely, manages timelinesAnalyzes data, creates reports, often works independently or in small teams remotely
Employer & Industry UsageTech, finance, healthcare companies managing data projects remotelyVarious industries, focusing on data analysis and reporting

The Remote Data Analytics Project Manager oversees data projects, coordinating teams and managing timelines, while Data Analysts focus on analyzing data and generating insights. Both roles often work remotely and require strong analytical skills, but project managers have additional responsibilities in leadership and project delivery.

What are popular job titles related to Remote Data Analytics Project Manager jobs in Illinois? For Remote Data Analytics Project Manager jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Remote Data Analytics Project Manager jobs in Illinois look for? The top searched job categories for Remote Data Analytics Project Manager jobs in Illinois are:
What cities in Illinois are hiring for Remote Data Analytics Project Manager jobs? Cities in Illinois with the most Remote Data Analytics Project Manager job openings:
PMO Data Analyst

PMO Data Analyst

MustardSeed

Chicago, IL โ€ข Remote

Full-time

Medical, Retirement, PTO

Posted 16 days ago


Job description

Job Description: PMO Data Analyst


Location: Remote, US Only


About the Role:
MustardSeed is seeking a PMO Data Analyst to help turn project data, client artifacts, schedules,
risk logs, vendor performance history, and lessons learned into actionable insight that improves
how we plan, execute, and advise across client engagements.


This role will work closely with project managers and PMO leaders to identify trends in project
performance, quantify what causes projects to run fast or slow, and create reusable benchmarks
that can be applied across clients and industries. The PMO Data Analyst will help MustardSeed
move from individual project learnings to portfolio-level intelligence, giving our teams stronger
evidence to support planning, forecasting, vendor recommendations, and delivery decisions.
Over time, this role is also expected to help us move from mainly reporting on what already
happened toward building early-warning signals that improve projects while they are still
running.


Responsibilities include (but are not limited to):

  • Analyze project schedules, baselines, actuals, milestones, and timelines to identify where
    projects gain or lose time.
  • Compare schedule performance across projects, clients, industries, phases, task types,
    and engagement models.
  • Identify recurring causes of schedule variance, including scope changes, approval delays,
    resource constraints, handoff issues, vendor delays, or client-side bottlenecks.
  • Measure how long key decisions and approvals take to make and help quantify the cost
    of delay so that slow decisions can be identified and acted on earlier.
  • Analyze supplier and vendor performance data, including cycle times, on-time delivery,
    rework rates, responsiveness, and recurring delay patterns.
  • Review risk registers and issue logs to determine which risk categories most often
    materialize, which mitigation strategies are most effective, and whether risk scoring
    accurately predicts real project outcomes.
  • Support critical path and dependency analysis by identifying where bottlenecks,
    handoffs, and sequencing issues create project delays.
  • Build dashboards, reports, and data visualizations that help internal teams and client
    stakeholders understand project performance, risks, and forecasts.
  • Design dashboards and reports so that they give project teams something useful in
    return, such as early sight of upcoming pressure points, and are used to support and
    guide delivery rather than to assign blame for missed dates.
  • Develop reusable benchmarks and playbooks by client type, project size, industry,
    project phase, or service offering.
  • Maintain these benchmarks and patterns as a single, reusable evidence base, keeping it
    documented and regularly reviewed so that only patterns the data continues to support
    are kept in use.
  • Distinguish meaningful project performance patterns from one-off issues or data noise,
    helping teams focus on the drivers that most consistently impact schedule performance.
  • Support post-project reviews by quantifying lessons learned and translating findings into
    practical recommendations for future engagements. Where the same pattern is
    confirmed across enough projects, turn these findings into reusable early-warning checks
    that flag the same risk on current and future engagements before it materializes, and
    update or retire those checks as new project data either supports or disproves them.
  • Partner with project teams to improve data quality, consistency, and usability across
    project management tools and reporting processes.
  • Where practical, set up repeatable or automated data feeds from project management,
    schedules, and finance tools so that performance data can be refreshed on a regular
    basis rather than pulled together manually for each report.
  • Ensure that any data or benchmarks reused across clients are appropriately anonymized
    and handled in line with client confidentiality and data protection requirements.
  • Help create forecasting models or early-warning indicators related to schedule risk,
    completion probability, vendor performance, or project health.
  • Present findings in a clear, practical way that enables project managers, consultants, and
    leadership to make better decisions.


Required Qualifications:

  • 3 to 6 years of experience in data analysis, project analytics, PMO reporting, project
    controls, operations analytics, supply chain analytics, business intelligence, or a related
    role.
  • Strong Excel and SQL skills, including pivot tables, formulas, data cleaning, querying, and
    analysis.
  • Experience with data visualization tools such as Power BI or Tableau, with the ability to
    build clear, actionable dashboards for stakeholders.
  • Strong written and verbal communication skills, with the ability to translate data into
    clear, practical insights for project managers, client leadership, and executive-level
    stakeholders.
  • Working knowledge of statistics and the ability to apply basic statistical concepts to
    identify trends, variance, and performance patterns. This includes an understanding of
    probability, ranges, and confidence levels, and an interest in probabilistic or
    reference-based forecasting methods such as Monte Carlo or reference-class
    forecasting.


Preferred Qualifications:

  • Bachelor's degree preferred in data analytics, business analytics, statistics, operations, or
    related field.
  • Experience working with the data layer beneath project management platforms,
    including exported project data, connected reporting tools, or structured datasets from
    PM systems.
  • Experience automating data preparation or building repeatable data pipelines, for
    example using Python or a similar tool, is an advantage.
  • Exposure to Lean, Six Sigma, process improvement, forecasting, or operational
    excellence.
  • Familiarity with project management concepts such as baseline vs. actuals, critical path
    method (CPM), schedule variance, milestone tracking, earned value management (EVM),
    risk registers, issue logs, and lessons learned.
  • Experience with Smartsheet, MS Project, Microsoft Planner, Asana, Monday.com, Jira, or
    other project management platforms.
  • Exposure to regulated or life sciences project environments, and the data, stage gates,
    and milestone structures used in them, is helpful but not essential.


Compensation & Benefits: Salary is commensurate with experience. We offer comprehensive benefits including a company sponsored Individual Coverage Healthcare Reimbursement Arrangement (ICHRA), 401(K), Healthcare Savings Account (HSA), Paid Time Off (PTO), Parental Leave, and paid company holidays, Additionally, MustardSeed offers robust support for our team members' professional development, employee incentives, and hands-on experience in a wide variety of projects and environments.



Additional Information: MustardSeed provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, sexual orientation, gender identity or expression, or any other characteristic protected by the law. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.