Ag Data Analyst

Moose Jaw - SK - 8-54 Stadacona St. W, Canada

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Company Overview

At Parametrics.Ag, we're redefining the future of agricultural insurance through the power of data science, software innovation, and industry-leading analytics. With deep roots in agriculture and decades of combined experience in risk modeling, underwriting strategy, geospatial intelligence, and climate science, we’re building the next generation of parametric insurance products tailored for modern farming.

Our proprietary platforms integrate cutting-edge predictive analytics, remote sensing technologies, and hyper-local climate data to provide precise, transparent, and scalable solutions for producers and insurers alike. From developing advanced underwriting models to deploying intuitive digital tools for customers, every product we build reflects our commitment to innovation, accuracy, and usability.

We’re proud to house a brilliant team of experts in machine learning, actuarial science, agronomy, economics, and full-stack software development—all working in a fast-paced, creatively driven environment. At Parametrics.Ag, your contributions don’t just support a project—they help shape a more resilient and sustainable agricultural ecosystem.

Join us on our mission to bring clarity, confidence, and innovation to agricultural risk management.

Role Overview

As an Ag Data Analyst, you will support our Data Science team by acquiring, preprocessing, and analyzing remote-sensing and geospatial datasets. You’ll help turn raw satellite data into actionable maps, insights, and model inputs that power our predictive analytics, parametric insurance products and agricultural production risk assessments.

Key Responsibilities

  • Data Acquisition & Management
    • Download and organize satellite imagery data from public APIs and commercial providers
    • Work with diverse geospatial formats: GeoTIFF, netCDF, GeoParquet, shapefiles, rasters

  • Data Processing & Analysis
    • Develop Python scripts for ETL workflows using pandas, NumPy, xarray, and geopandas
    • Perform spatial joins, reprojections, tiling, and subsetting of large rasters
    • Generate new indices, time-series analysis, and other remote-sensing products

  • Visualization & Reporting
    • Create maps and dashboards to communicate spatial analysis
    • Document data pipelines, methodologies, and quality-control procedures

  • Collaboration & Learning
    • Work closely with the Data Science Manager and data scientists on GIS components.
    • Participate in code reviews, knowledge-sharing sessions, and team meetings

Required Qualifications

  • Bachelor’s degree in Geography, GIS/Remote Sensing, Computer Science, Environmental Science, Economics, Finance or related field
  • Hands-on experience with:
    • Python ecosystem: pandas, NumPy, geopandas, xarray/netCDF4
    • Reading/writing GeoTIFF, netCDF, GeoParquet, and vector formats
    • Using GIS libraries and tools (GDAL/OGR, rasterio, Fiona)
  • Familiarity with command-line tools and version control (Git)
  • Strong problem-solving skills and attention to data quality

Assets (Nice to Have)

  • Coursework or experience in Financial or Economic data analysis
  • Exposure to cloud platforms (AWS S3, Lambda)
  • Basic understanding of agricultural processes and insurance products

Department Account Management & Service

Required Experience: 1-2 years of relevant experience

Required Travel: No Travel Required

We endeavor to make this website accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact the recruiting team HUBRecruiting@hubinternational.com. This contact information is for accommodation requests only; do not use this contact information to inquire about the status of applications.

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

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Category: Analyst Jobs

Tags: APIs AWS Computer Science Data analysis Data pipelines Data quality Economics ETL Finance Git Lambda Machine Learning NumPy Pandas Pipelines Python

Region: North America
Country: Canada

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