Mid-level Data Scientist – Image Geoprocessing, GIS

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Design and execute machine learning models along with statistical methods tailored to agribusiness challenges.

• Evaluate geospatial, agronomic, climatic, operational, and market datasets.

• Process satellite imagery, aerial photographs, and various remote sensing data sources.

• Develop models for classification, segmentation, forecasting, pattern recognition, and insights generation.

• Create and enhance geoprocessing algorithms to automate data analyses and workflows.

• Utilize GIS tools for the analysis, interpretation, and visualization of spatial data.

• Integrate information from APIs, databases, files, geospatial platforms, and external data sources.

• Establish processes for data preparation, validation, and enhancement.

• Assist in developing KPIs, dashboards, thematic maps, and analytical outputs.

• Evaluate the quality, performance, and relevance of the models developed.

• Document methodologies, assumptions, outcomes, and limitations related to analyses.

• Create reports and presentations highlighting key findings and recommendations.

• Collaborate with technology, data, business specialists, and client teams.

• Convert business requirements into analytical hypotheses and data-driven solutions.

• Facilitate the deployment and refinement of models in production settings.

⛳️ Requirements

• A Bachelor's degree (or higher) in Data Science, Computer Science, Statistics, Mathematics, Engineering, Geography, Agronomy, Geoprocessing, or related disciplines.

• Consideration will also be given to candidates from alternative backgrounds with relevant experience.

• Proven experience in developing machine learning models or conducting statistical analyses.

• Proficiency in Python or R for data handling, modeling, and analysis.

• Familiarity with libraries like Pandas, NumPy, Scikit-learn, or similar.

• Experience with geospatial data, spatial analysis, or geoprocessing techniques.

• Knowledge of tools such as QGIS, ArcGIS, or equivalent platforms.

• Understanding of SQL and relational database systems.

• Capability to prepare, clean, validate, and analyze various datasets.

• Ability to interpret findings and communicate results effectively.

• Familiarity with model evaluation and validation metrics.

• Comfortable collaborating within multidisciplinary teams.

• Ability to comprehend business challenges and translate them into analytical strategies.

• Experience in processing and analyzing satellite imagery or aerial photographs.

• Familiarity with remote sensing and vegetation indices.

• Knowledge of geospatial libraries such as GeoPandas, Rasterio, GDAL, Shapely, or their equivalents.

• Experience with Google Earth Engine or other geospatial processing platforms.

• Familiarity with spatial databases like PostgreSQL/PostGIS.

• Knowledge of deep learning and computer vision methodologies.

• Proficiency in frameworks like TensorFlow, PyTorch, or Keras.

• Experience with time series models, forecasting, and anomaly detection.

• Familiarity with cloud computing platforms such as AWS, Google Cloud, or Microsoft Azure.

• Experience with Databricks, Spark, or other distributed processing technologies.

• Understanding of Git and code versioning practices.

• Knowledge of APIs, containers, and the fundamentals of model deployment.

• Proficiency in visualization tools such as Power BI, Tableau, Matplotlib, or Plotly.

• Familiarity with MLOps practices, monitoring, and model lifecycle management.

• Experience in agribusiness-related projects.

• Understanding of agricultural production, climate, soils, crops, farm operations, or supply chains.

• Experience with crop yield forecasting models or field monitoring.

• Knowledge of meteorological, agronomic, or territorial data.

• Experience in land use and land cover analysis.

• Understanding of spatial statistics and time series analysis.

• Experience in deploying models within corporate production environments.

• Completion of courses, specializations, or certifications in Data Science, Artificial Intelligence, Geoprocessing, Remote Sensing, or Agribusiness.

🏝️ Benefits

• All Points (points program for flights and accommodations).

• Medical insurance.

• Dental insurance.

• Life insurance.

• Pet care plan.

• Mobility support (including fuel allowance and rideshare options such as Uber, 99).

• Reimbursement Flex (covering glasses, vaccines, education, events, and other expenses).

• Meal vouchers and/or food allowances.

• Birthday off.

• Wellhub (Gympass).

• Alelo Club offering various store and pharmacy discounts.

• 180-day maternity leave.

• 30-day paternity leave.

• Childcare assistance during the child's first year.

• Transportation allowance based on project or area requirements.

• Annual bonus program.

• Commission for referring new clients.

• Access to a global TRAINING and DEVELOPMENT center with current market content and courses.

• Flex Time – a dedicated weekly period for your development.

• Mentorship programs with seasoned leaders.

• Semi-annual performance assessments.

• A culture of feedback and regular 1:1s with your manager and team.

• Support in creating a customized development plan aligned with your objectives.

• Darwin – a global initiative to nurture young talent, enhancing both soft and hard skills.

• Grounding – an international onboarding program for new professionals initiating their consulting careers.

• Induction – a global onboarding experience led by HR and business leaders to introduce BIP's culture, values, purpose, and global programs.

• Spinnaker – training aimed at enhancing strategic skills for Managers and Senior Managers, aiding business development within a complex ecosystem.