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Entry Level Data Analyst R Programming Jobs in Toronto, ON

Supply Chain Data Analyst Primary Job Location: Toronto, ON, Canada Location Flexibility: Hybrid ... You sit between the data and the engineers. You have to translate what you see into something a dev ...

Product Data Analyst

Toronto, ON · Remote

$145K - $175K/yr

Our data engineering team owns the pipelines; you'll focus on the analysis, the recommendation, and the decision that follows. What you'll own The metrics that run the business. You'll own the ...

Data Operations Analyst (Contract)

Toronto, ON · On-site

CA$72K - CA$108K/yr

As a Data Operations Analyst, Investment Data Operations, you will help deliver accurate, timely ... SQL, Python, Power BI, and workflow tools such as Azure DevOps are assets. This posting is for an ...

Perform initial data analysis to assess project feasibility and provide actionable feedback for developers, working closely with the development team to ensure design and development outputs meet key ...

Showing results 21-40

Entry Level Data Analyst R Programming information

What is the difference between Entry Level Data Analyst R Programming vs Data Scientist?

AspectEntry Level Data Analyst R ProgrammingData Scientist
Required SkillsBasic R programming, data cleaning, visualization, ExcelAdvanced R, Python, machine learning, statistical modeling
Work EnvironmentBusiness, finance, marketing teamsResearch, tech, healthcare, diverse industries
CertificationsData analysis, R programming coursesData science, machine learning certifications

Entry Level Data Analyst R Programming roles focus on data cleaning, visualization, and basic analysis using R, often within business environments. Data Scientists require advanced statistical and programming skills, including machine learning, and work on complex predictive models across various industries. While both roles involve data handling, Data Scientists typically have a broader skill set and handle more complex projects.

What is an entry level data analyst r programming?

An Entry Level Data Analyst (R Programming) is a professional who uses the R programming language to collect, process, and analyze data to help organizations make informed decisions. They typically work with large datasets, create visualizations, and generate reports under the guidance of more experienced analysts. Entry-level data analysts are often responsible for basic data cleaning, statistical analysis, and supporting team projects while they develop their skills in R and data analysis techniques.

What skills and qualifications are needed to thrive as an entry level data analyst r programming?

To thrive as an Entry Level Data Analyst specializing in R Programming, you need a solid grounding in statistics, data cleaning, and analytical methods, typically supported by a relevant degree such as statistics, mathematics, or computer science. Proficiency in R programming, familiarity with data visualization tools (e.g., ggplot2), and experience with spreadsheet software or SQL are commonly required. Strong attention to detail, problem-solving abilities, and clear communication skills set outstanding candidates apart in this role. These skills are crucial to accurately interpret data, deliver actionable insights, and effectively collaborate with teams to support data-driven decision-making.

What are some typical challenges entry level data analysts face when working with R programming in a team setting?

Entry-level data analysts using R often encounter challenges such as adapting to existing codebases, understanding team-specific data workflows, and ensuring code reproducibility and documentation for collaborative projects. New analysts may also need to quickly learn version control practices (like using Git) and follow standardized procedures for data cleaning and reporting. Regular communication with senior analysts and participation in code reviews are essential to build both technical proficiency and teamwork skills.

What are the most commonly searched types of Data Analyst R Programming jobs in Toronto, ON?

The most popular types of Data Analyst R Programming jobs in Toronto, ON are:

What are popular job titles related to Entry Level Data Analyst R Programming jobs in Toronto, ON?

For Entry Level Data Analyst R Programming jobs in Toronto, ON, the most frequently searched job titles are:

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The top searched job categories for Entry Level Data Analyst R Programming jobs in Toronto, ON are:

Infographic showing various Entry Level Data Analyst R Programming job openings in Toronto, ON as of July 2026, with employment types broken down into 1% As Needed, 80% Full Time, 15% Part Time, 1% Temporary, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Supply Chain Data Analyst

Fulfillment IQ

Toronto, ON • On-site

Full-time

Medical, Dental, Retirement, PTO

Posted 4 days ago


Job description

Job Title: Supply Chain Data Analyst

Primary Job Location: Toronto, ON, Canada

Location Flexibility: Hybrid, Toronto based

Employment Type (Permanent/Contract/Part-time/Intern): Permanent, Full-Time

Hiring Timeline (Hiring month): Immediate

Reporting Line: Chief Scientist

Existing Vacancy: Yes

Application Deadline: Open until filled

Requirements

About Fulfillment IQ (FIQ)

Fulfillment IQ is a supply chain engineering and transformation company that helps brands, retailers, and 3PLs design, build, and scale high-performance logistics operations.

We work at the intersection of strategy, supply chain, and technology where we solve complex, real-world problems across warehouse design, automation, order management, transportation, and end-to-end supply chain execution.

Our teams combine deep domain expertise with strong technical capability, delivering outcomes through consulting, systems implementation, and proprietary platforms that accelerate time-to-value and reduce delivery risk.

If you enjoy working in complex environments, partnering closely with clients, and seeing your work make a tangible impact on how global commerce moves, this is the place where your skills and judgment truly come to life.

Role Overview

You will drive the data analytics capabilities of Workbench, FIQ's warehouse design platform, by automating the processing, cleaning and analysis of large datasets from warehouse and logistics operations. You will bring a data-centric and product-oriented mindset to Workbench, working hand-in-hand with our domain experts and software engineering team. As a result, you will help turn data-driven insights into product features that automate time-consuming and error-prone tasks which, today, are mostly performed manually in spreadsheets.

What You'll Do

  • Process, clean and analyze large customer supply chain datasets, for example a year of 3PL order data, and extract relevant insights that can be turned into valuable decisions.
  • Identify patterns and similarities across heterogeneous datasets and consolidate and automate our data analytics capabilities across our customer base.
  • Identify which analyses matter most and how it should be presented, be it a metric, a graph, or something else. While you will not be expected to build production-grade UI components, you will develop data visualization demos and prototypes.
  • Help get Workbench to the point where users answer most of their own data questions inside the platform instead of in a spreadsheet, in a fraction of the time it currently takes them.
  • Lean hard on AI to move fast through the execution and spend your judgment on what is worth automating. We are a small but mighty team, and that is our DNA.

The Honest Version of the Hard Parts

  • We work with real, large, and messy data. You will spend real time organizing, cleaning and reconciling datasets before you get to the interesting part, and you have to be honest about what you trust.
  • The manual analysis is the means, not the job. If you just want to churn through datasets forever, this is the wrong seat. The whole point is to see the repeated pattern and get it built into the product.
  • The goal is not to repeat a manual process repeatedly: it is to build comprehensive capabilities that can automate that process as broadly and as efficiently as possible.
  • You sit between the data and the engineers. You have to translate what you see into something a dev team can build, and hold the thread on what capabilities will bring the most value to our users.
  • Domain knowledge matters more than proficiency in specific data processing tools. If you do not know supply chain or logistics, you will feel it, because the analysis only means something in context.

Where This Goes

Think chessboard, not ladder. You will start by owning the data-analysis capability inside Workbench, and there is real room to grow into data-product ownership and into shaping how the whole platform makes sense of supply chain data. You will work directly with the Chief Scientist. Tell us what you want to build and we will help you get there.

How We'll Interview You

  • Intro conversation: a short call to swap context and make sure the basics line up.
  • Conversation with the Chief Scientist: how you think about data and supply chain, and whether we can make each other better.
  • Technical: a live working session on a real, messy supply chain dataset. Clean it, find the patterns that matter, and tell us what you would automate and how you would present it.
  • Final: discussion with the Workbench team.

What You Need to Have

  • Supply chain or logistics domain experience. We would rather have someone who knows the operation and can pick up the data tools than a strong analyst who has never seen supply chain data.
  • Fluency with at least one tabular-data tool (e.g. pandas, Polars, Arrow). You have worked with real, messy data end to end, not just tidy classroom sets.
  • The business sense to see the repeated pattern and turn it into a clear ask for an engineering team, and the communication to present it well.
  • Enough visualization judgment to say what to show and how, even though you will not build the front end.
  • A Bachelor's in Data Science, Statistics, Industrial Engineering, or a related quantitative field. We care more about how you think and your domain than your exact number of years.
  • Nice to have: Warehouse, fulfillment, or order-management operations exposure.
  • Nice to have: Experience working shoulder to shoulder with a product or engineering team.
  • Nice to have: Tableau or Power BI, or GPU dataframe tooling.

Why You Will Love Working Here

Real ownership on a team small enough that your judgment shapes what gets built, working directly with the Chief Scientist on a product that is genuinely new. You will build the capability, not inherit a dashboard and babysit it. We move fast, we automate the busywork, and we back people who take initiative.

Compensation and Benefits

FIQ posts good-faith pay ranges. The base salary range below reflects experience, location, and internal equity.

Canada (Ontario)

Base salary range: 85,000K - 110,000K per year.

Health and Wellness

  • Comprehensive extended health and dental coverage for you and your family
  • Employee wellness programs where applicable

Time Off

  • Competitive paid time off, sick leave, and public holidays
  • Flexible leave policies that respect local labour standards

Retirement and Financial Security

  • CPP contributions and a group retirement savings plan with employer contributions
  • Employee stock options (ESOP), where applicable

Professional Growth

  • Dedicated learning and development budget, with support for skills, leadership, and career progression

Flexible Work

  • Remote and hybrid work options, flexible hours aligned to role and client needs

Additional Perks

  • Equipment and workstation allowances, internet and business travel reimbursements, team events and offsites

Work Authorization

Applicants must be legally authorized to work in Canada.

Use of Artificial Intelligence in Hiring: 

FIQ uses artificial intelligence to assist in the screening, assessment, and shortlisting of applications for this position, including features within our applicant tracking system. AI supports human reviewers and does not make hiring decisions on its own. Final hiring decisions are made by FIQ personnel. If you have questions about how AI is used in this process, contact hr@fulfillmentiq.com. 

Accommodations: 

FIQ is committed to an inclusive and accessible recruitment process. Consistent with the Accessibility for Ontarians with Disabilities Act (AODA) and the Ontario Human Rights Code, accommodations are available on request at any stage of recruitment and assessment. Contact hr@fulfillmentiq.com and we will work with you to meet your needs. 

Candidate Privacy: 

Your personal information is collected and used for recruitment purposes in accordance with FIQ's Candidate Privacy Notice, available here, and in accordance with applicable Canadian privacy law (PIPEDA). This includes the use of AI-assisted screening described above and cross-border storage or processing that may occur in the United States. By applying, you acknowledge this notice. Questions: hr@fulfillmentiq.com. 

Equal Opportunity: 

Fulfillment IQ is a people-first company built on trust, collaboration, and ownership. We are proud to be an equal opportunity employer and are committed to building a diverse, inclusive, and high-performing workplace. We consider all qualified applicants without regard to any ground protected under the Ontario Human Rights Code, including race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, or disability. 

Learn More About Us:

Website: fulfillmentiq.com

LinkedIn: Fulfillment IQ

Spotify: eCom Logistics Podcast Spotify

YouTube: eCom Logistics Podcast YouTube