Description :
As Head of Data Science & Remote Sensing, you will own the building, designing and execution of the Groups in house engine remote sensing and Earth Observation (EO) strategy. You will lead a multidisciplinary team of data scientists, remote sensing engineers, and geospatial analysts to convert multi-sensor satellite imagery (SAR, optical, thermal) and weather / soil datasets into farmer-ready insights. Your mandate is to build scientifically rigorous yet production-grade pipelines that power the products while mentoring talent and institutionalizing calibration, validation, and quality assurance frameworks.
High Output Management Principles Applied
- Focus on managerial leverage : hire exceptional leads, establish rituals, and eliminate bottlenecks.
- Define output metrics : uptime, farmer adoption, model accuracy, data pipeline SLAs.
- Task-relevant maturity : delegate decisions based on individual competence & experience.
- Build scalable systems instead of relying on individual heroics.
Job Summary :
Builds and scales EO / RS data pipelines for agriculture, ensuring robust scientific credibility and operational deployment. Owns end-to-end pipelines from raw SAR / optical / thermal imagery to calibrated farmer insights, powering Products with accuracy, reliability, and interpretability.
Roles & Responsibilities :
Architect, build, and scale pipelines : Sentinel-1 SAR preprocessing, optical fusion, gap-filling, ET estimation (SEBAL / energy balance), soil moisture, stress detection, and crop phenology.Own EO / ML model lifecycle : design, training, validation, deployment, monitoring, drift detection, and retraining cadence.Define validation metrics(R / MAE / F1 / mAP),; establish calibration plans, ground-truth sampling, and error budgets for each crop and district.Partner with product and engineering to expose insights via APIs and app dashboards; ensure farmer-facing confidence thresholds.Stand up rigorous Cal / Val (Calibration / Validation) programs : agronomy inputs, field data partnerships, and A / B experiments.Recruit, mentor, and scale a team of EO / ML scientists; drive OKRs, documentation, and knowledge-sharing.Establish labeling / QA SOPs, build golden datasets, and adopt active learning for annotation efficiency.Manage compute and storage tradeoffs; design for scalability while controlling costs.Lead collaborations with academia, space agencies, and EO labs to accelerate innovation and maintain technical edge.Architect, build, and scale multi-sensor satellite data pipelines (SAR, optical, thermal), ground ingestion, preprocessing, fusion, and real-time analytics.Facilitate buildbuypartner decisions; manage vendors and strategic tech partnerships (academia, space agencies, hyperscalers).Stand up rigorous Cal / Val programs with ground truth, agronomy inputs, and A / B experimentation frameworksTranslate business needs into technical roadmaps and quarterly release plans; align with CEO / CTO and Business HeadsCandidate Profile & Skill Requirements :
Leadership & Scale : 8+ years in Earth Observation and Remote Sensing applied to agriculture.Strong expertise in SAR, optical, and thermal satellite data processing : Sentinel-1 / 2, Landsat, MODIS Proficiency in SEBAL / energy balance models, ET estimation, stress / yield proxies.Hands-on experience with geospatial stacks : GDAL / Rasterio, xarray / dask, GeoPandas, PostGIS, STAC catalogs.Technology Leadership :
Skilled in PyTorch / TensorFlow; ML pipeline development with MLflow / W&B; validation at scale.Demonstrated success in deploying ML for EO : time-series analysis, computer vision, deep learning on geospatial data.Experience with retraining strategies, drift management, and large-scale error calibration.MLOps & Platforms :
Proficient in Airflow / Prefect, Docker / Kubernetes, scalable inference design.Strong knowledge of COG / Zarr formats, cloud-native data management, and API delivery of insights.Innovation & IP :
Demonstrated experience in scientific publications, patents, or invention disclosures in EO / ML.Proven track record of building novel methods for EO data analysis and validation.Industry Knowledge (Preferred) :
AgTech analytics and parametric / index insurance data pipelines.Rainfall downscaling, crop simulation model coupling, and ground-truth calibration.Operational constraints of EO systems : revisit cycles, cloud interference, latency management.Personal Attributes :
Hands-on scientist with the ability to think strategically.Integrity, resilience, and ownership mindset.Highly organized, detail-oriented, frugal with compute, and results-focused.(ref : hirist.tech)